Machine learning - Knowledge

997: 2107: 3773:" poses another yet significant challenge. Black box refers to a situation where the algorithm or the process of producing an output is entirely opaque, meaning that even the coders of the algorithm cannot audit the pattern that the machine extracted out of the data. The House of Lords Select Committee, which claimed that such an "intelligence system" that could have a "substantial impact on an individual’s life" would not be considered acceptable unless it provided "a full and satisfactory explanation for the decisions" it makes. 2617:

remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference from many other statistical classification problems is the inherently unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set and then test the likelihood of a test instance to be generated by the model.

2942: 3260: 2161:. Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. As an example, for a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email. Examples of regression would be predicting the height of a person, or the future temperature. 3202: 13020: 12017: 1773:, and compression-based similarity measures compute similarity within these feature spaces. For each compressor C(.) we define an associated vector space ℵ, such that C(.) maps an input string x, corresponding to the vector norm ||~x||. An exhaustive examination of the feature spaces underlying all compression algorithms is precluded by space; instead, feature vectors chooses to examine three representative lossless compression methods, LZW, LZ77, and PPM. 4019:

is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is potential for machine learning in health care to provide professionals an additional tool to diagnose, medicate, and plan recovery paths for patients, but this requires these biases to be mitigated.

3144: 11997: 2426:(CAA). It is learning with no external rewards and no external teacher advice. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. The system is driven by the interaction between cognition and emotion. The self-learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning routine: 13030: 11096: 2460:

two environments, one is the behavioral environment where it behaves, and the other is the genetic environment, wherefrom it initially and only once receives initial emotions about situations to be encountered in the behavioral environment. After receiving the genome (species) vector from the genetic environment, the CAA learns a goal-seeking behavior, in an environment that contains both desirable and undesirable situations.

13040: 1638: 3055: 4099:. Running machine learning model in embedded devices removes the need for transferring and storing data on cloud servers for further processing, henceforth, reducing data breaches and privacy leaks happening because of transferring data, and also minimizes theft of intellectual properties, personal data and business secrets. Embedded Machine Learning could be applied through several techniques including 1574:" button to cause it to reevaluate incorrect decisions. A representative book on research into machine learning during the 1960s was Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification. Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973. In 1981 a report was given on using teaching strategies so that an 4005:

out by the Computing Research Association (CRA) in 2021, "female faculty merely make up 16.1%" of all faculty members who focus on AI among several universities around the world. Furthermore, among the group of "new U.S. resident AI PhD graduates," 45% identified as white, 22.4% as Asian, 3.2% as Hispanic, and 2.4% as African American, which further demonstrates a lack of diversity in the field of AI.

3868: 2146:, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs. An optimal function allows the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task. 2221: 3736:, predicted that 80% of medical doctors jobs would be lost in the next two decades to automated machine learning medical diagnostic software. In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists. In 2019 3885:

not primarily make judgments from the spatial relationship between components of the picture, and they learn relationships between pixels that humans are oblivious to, but that still correlate with images of certain types of real objects. Modifying these patterns on a legitimate image can result in "adversarial" images that the system misclassifies.

3380:, low samples, and ambiguous class issues that standard machine learning approach tend to have difficulty resolving. However, the computational complexity of these algorithms are dependent on the number of propositions (classes), and can lead to a much higher computation time when compared to other machine learning approaches. 3884:

Learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses. A real-world example is that, unlike humans, current image classifiers often do

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Explainable AI (XAI), or Interpretable AI, or Explainable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the decisions or predictions made by the AI. It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI

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Different machine learning approaches can suffer from different data biases. A machine learning system trained specifically on current customers may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on human-made data, machine learning

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Machine Learning is becoming a useful tool to investigate and predict evacuation decision making in large scale and small scale disasters. Different solutions have been tested to predict if and when householders decide to evacuate during wildfires and hurricanes. Other applications have been focusing

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published the first research book created using machine learning. In 2020, machine learning technology was used to help make diagnoses and aid researchers in developing a cure for COVID-19. Machine learning was recently applied to predict the pro-environmental behavior of travelers. Recently, machine

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Other forms of ethical challenges, not related to personal biases, are seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there

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Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules

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A core objective of a learner is to generalize from its experience. Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability

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on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the preassigned labels of a

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and documentation of algorithmic rules used by a system is considered a critical part of machine learning, some researchers blame lack of participation and representation of minority population in the field of AI for machine learning's vulnerability to biases. In fact, according to research carried

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that, after being "trained" on a given dataset, can be used to make predictions or classifications on new data. During training, a learning algorithm iteratively adjusts the model's internal parameters to minimize errors in its predictions. By extension, the term "model" can refer to several levels

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It is a system with only one input, situation, and only one output, action (or behavior) a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in

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Unsupervised learning algorithms find structures in data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of

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Modern-day machine learning has two objectives. One is to classify data based on models which have been developed; the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with

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had been using a computer program trained from data of previous admissions staff and that this program had denied nearly 60 candidates who were found to either be women or have non-European sounding names. Using job hiring data from a firm with racist hiring policies may lead to a machine learning

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organization, a machine learning algorithm's insight into the recidivism rates among prisoners falsely flagged "black defendants high risk twice as often as white defendants." In 2015, Google Photos would often tag black people as gorillas, and in 2018, this still was not well resolved, but Google

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in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial

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algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying

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For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has under fitted the data. If the complexity of the model is increased in

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Conventional statistical analyses require the a priori selection of a model most suitable for the study data set. In addition, only significant or theoretically relevant variables based on previous experience are included for analysis. In contrast, machine learning is not built on a pre-structured

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algorithm. Sparse dictionary learning has been applied in several contexts. In classification, the problem is to determine the class to which a previously unseen training example belongs. For a dictionary where each class has already been built, a new training example is associated with the class

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Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data but penalizing the theory in accordance with how complex the theory is.

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Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results. Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and

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to win the Grand Prize in 2009 for $ 1 million. Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly. In 2010 The Wall Street

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Machine learning has been used as a strategy to update the evidence related to a systematic review and increased reviewer burden related to the growth of biomedical literature. While it has improved with training sets, it has not yet developed sufficiently to reduce the workload burden without

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Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensory data has not yielded attempts to algorithmically define

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to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a

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Three broad categories of anomaly detection techniques exist. Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal, by looking for instances that seem to fit the least to the

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In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts of inactivity. This pattern does not adhere to the common statistical definition of an outlier as a rare object. Many outlier detection methods (in

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and cluster analysis. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions. This

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that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers.

2285:(with completely labeled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy. 3405:

is something to watch out for when training a machine learning model. Trained models derived from biased or non-evaluated data can result in skewed or undesired predictions. Biased models may result in detrimental outcomes, thereby furthering the negative impacts on society or objectives.

1784:, the best possible compression of x is the smallest possible software that generates x. For example, in that model, a zip file's compressed size includes both the zip file and the unzipping software, since you can not unzip it without both, but there may be an even smaller combined form. 3888:

Adversarial vulnerabilities can also result in nonlinear systems, or from non-pattern perturbations. For some systems, it is possible to change the output by only changing a single adversarially chosen pixel. Machine learning models are often vulnerable to manipulation and/or evasion via

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that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction. Rule-based machine learning approaches include

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learning technology was also applied to optimize smartphone's performance and thermal behavior based on the user's interaction with the phone. When applied correctly, machine learning algorithms (MLAs) can utilize a wide range of company characteristics to predict stock returns without

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can be placed undetectably into classifying (e.g., for categories "spam" and well-visible "not spam" of posts) machine learning models that are often developed and/or trained by third parties. Parties can change the classification of any input, including in cases for which a type of

2236:) so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some 1765:

on the output distribution). Conversely, an optimal compressor can be used for prediction (by finding the symbol that compresses best, given the previous history). This equivalence has been used as a justification for using data compression as a benchmark for "general intelligence".

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Data compression aims to reduce the size of data files, enhancing storage efficiency and speeding up data transmission. K-means clustering, an unsupervised machine learning algorithm, is employed to partition a dataset into a specified number of clusters, k, each represented by the

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Zhang, Bosen; Huang, Haiyan; Tibbs-Cortes, Laura E.; Vanous, Adam; Zhang, Zhiwu; Sanguinet, Karen; Garland-Campbell, Kimberly A.; Yu, Jianming; Li, Xianran (2023-02-13). Streamline unsupervised machine learning to survey and graph indel-based haplotypes from pan-genomes (Report).

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to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example,

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techniques. Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP and are used when exact models are infeasible. Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent.

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Given a set of observed points, or input–output examples, the distribution of the (unobserved) output of a new point as function of its input data can be directly computed by looking like the observed points and the covariances between those points and the new, unobserved point.

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Recent advancements in machine learning have extended into the field of quantum chemistry, where novel algorithms now enable the prediction of solvent effects on chemical reactions, thereby offering new tools for chemists to tailor experimental conditions for optimal outcomes.

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laid the initial theoretical foundation for inductive machine learning in a logical setting. Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples. The term

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methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category. An SVM training algorithm is a

3225:(DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform 2848:

as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that

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consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.

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reportedly was still using the workaround to remove all gorillas from the training data and thus was not able to recognize real gorillas at all. Similar issues with recognizing non-white people have been found in many other systems. In 2016, Microsoft tested

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found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional

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knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.

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method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods,

2601:, anomaly detection, also known as outlier detection, is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically, the anomalous items represent an issue such as 1727:

Machine learning (ML), reorganized and recognized as its own field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the

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arrived at a specific decision. By refining the mental models of users of AI-powered systems and dismantling their misconceptions, XAI promises to help users perform more effectively. XAI may be an implementation of the social right to explanation.

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AI can be well-equipped to make decisions in technical fields, which rely heavily on data and historical information. These decisions rely on objectivity and logical reasoning. Because human languages contain biases, machines trained on language

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Bozinovski, S. (1982). "A self-learning system using secondary reinforcement". In Trappl, Robert (ed.). Cybernetics and Systems Research: Proceedings of the Sixth European Meeting on Cybernetics and Systems Research. North-Holland. pp. 397–402.

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is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in

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estimated the hardware computing used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.

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Typically, machine learning models require a high quantity of reliable data to perform accurate predictions. When training a machine learning model, machine learning engineers need to target and collect a large and representative

2021:(PAC) model. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The 3332:

in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s. Conversely, machine learning techniques have been used to improve the performance of genetic and

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Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times.

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S. Bozinovski "Teaching space: A representation concept for adaptive pattern classification" COINS Technical Report No. 81-28, Computer and Information Science Department, University of Massachusetts at Amherst, MA, 1981.

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technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. This replaces manual

1835:, k-means clustering aids in data reduction by replacing groups of data points with their centroids, thereby preserving the core information of the original data while significantly decreasing the required storage space. 4569: 2993:, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a 2126:, consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an 7260:

Moringen, Alexandra; Fleer, Sascha; Walck, Guillaume; Ritter, Helge (2020), Nisky, Ilana; Hartcher-O'Brien, Jess; Wiertlewski, Michaël; Smeets, Jeroen (eds.), "Attention-Based Robot Learning of Haptic Interaction",

2074:: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end ( 10215: 10046: 5694: 2056:

Machine learning approaches are traditionally divided into three broad categories, which correspond to learning paradigms, depending on the nature of the "signal" or "feedback" available to the learning system:

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method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the

3719:" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from 3351:

The theory of belief functions, also referred to as evidence theory or Dempster–Shafer theory, is a general framework for reasoning with uncertainty, with understood connections to other frameworks such as

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distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.

3950:(TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used 1814:

can be utilized to compress data by grouping similar data points into clusters. This technique simplifies handling extensive datasets that lack predefined labels and finds widespread use in fields such as

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In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in

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Analytical and computational techniques derived from deep-rooted physics of disordered systems can be extended to large-scale problems, including machine learning, e.g., to analyze the weight space of

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is a potential result of data not being fully prepared for training. Machine learning ethics is becoming a field of study and notably, becoming integrated within machine learning engineering teams.

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particular, unsupervised algorithms) will fail on such data unless aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro-clusters formed by these patterns.

8073: 8925: 7499:, Research Report 192, Yale University, Department of Computer Science, 1981. Reprinted in J.-L. Lassez, G. Plotkin (Eds.), Computational Logic, The MIT Press, Cambridge, MA, 1991, pp. 199–254. 3364:. These theoretical frameworks can be thought of as a kind of learner and have some analogous properties of how evidence is combined (e.g., Dempster's rule of combination), just like how in a 3125:

exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the

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Zhang, Jun; Zhan, Zhi-hui; Lin, Ying; Chen, Ni; Gong, Yue-jiao; Zhong, Jing-hui; Chung, Henry S.H.; Li, Yun; Shi, Yu-hui (2011). "Evolutionary Computation Meets Machine Learning: A Survey".

8037: 3368:-based Bayesian approach would combine probabilities. However, there are many caveats to these beliefs functions when compared to Bayesian approaches in order to incorporate ignorance and 1509:

that calculated the winning chance in checkers for each side, the history of machine learning roots back to decades of human desire and effort to study human cognitive processes. In 1949,

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Other approaches have been developed which do not fit neatly into this three-fold categorization, and sometimes more than one is used by the same machine learning system. For example,

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is a process of reducing the number of random variables under consideration by obtaining a set of principal variables. In other words, it is a process of reducing the dimension of the

1885:" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, 4607:

Koza, John R.; Bennett, Forrest H.; Andre, David; Keane, Martin A. (1996). "Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming".

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used by computers to communicate data. Other researchers who have studied human cognitive systems contributed to the modern machine learning technologies as well, including logician

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results: Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time.

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method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".

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Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is

9345: 3997:'s predictive algorithm that resulted in "disproportionately high levels of over-policing in low-income and minority communities" after being trained with historical crime data. 3834:

in machine learning, that is, reducing bias in machine learning and propelling its use for human good, is increasingly expressed by artificial intelligence scientists, including

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supervised learning in order to train it to classify the cancerous moles. A machine learning algorithm for stock trading may inform the trader of future potential predictions.

9315: 9283: 9249: 4450: 1653:, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed " 8258: 5781: 10299:"Cornell & NTT's Physical Neural Networks: A "Radical Alternative for Implementing Deep Neural Networks" That Enables Arbitrary Physical Systems Training | Synced" 1954:

model; rather, the data shape the model by detecting underlying patterns. The more variables (input) used to train the model, the more accurate the ultimate model will be.

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caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation. By 1980,

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distinguished two statistical modeling paradigms: data model and algorithmic model, wherein "algorithmic model" means more or less the machine learning algorithms like

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Zhang, Bosen; Huang, Haiyan; Tibbs-Cortes, Laura E.; Vanous, Adam; Zhang, Zhiwu; Sanguinet, Karen; Garland-Campbell, Kimberly A.; Yu, Jianming; Li, Xianran (2023).

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that appeared later. Confer "Paraphrasing Arthur Samuel (1959), the question is: How can computers learn to solve problems without being explicitly programmed?" in

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or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximize.

7748: 932: 3745:. By employing effective feature engineering and combining forecasts, MLAs can generate results that far surpass those obtained from basic linear techniques like 1881:

in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "

10445: 10298: 7472: 4119:, Low-Rank Factorization, Network Architecture Search (NAS) & Parameter Sharing are few of the techniques used for optimization of machine learning models. 6681:

Bozinovski, Stevo (2014) "Modeling mechanisms of cognition-emotion interaction in artificial neural networks, since 1981." Procedia Computer Science p. 255-263

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being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to

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provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience

9455:"Crop yield prediction via explainable AI and interpretable machine learning: Dangers of black box models for evaluating climate change impacts on crop yield" 6773: 7954: 1256: 3993:

system duplicating the bias by scoring job applicants by similarity to previous successful applicants. Another example includes predictive policing company

1946:, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics. He also suggested the term 7866: 4503: 2486:

Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data. Examples include

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of specificity, from a general class of models and their associated learning algorithms to a fully trained model with all its internal parameters tuned.

1844:'s research with the Chinchilla 70B model. Developed by DeepMind, Chinchilla 70B effectively compressed data, outperforming conventional methods such as 879: 10246: 8769: 8515: 8065: 6690:

Bozinovski, S. (2001) "Self-learning agents: A connectionist theory of emotion based on crossbar value judgment." Cybernetics and Systems 32(6) 637–667.

2292:, the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets. 11139: 9638: 9082:

Caliskan, Aylin; Bryson, Joanna J.; Narayanan, Arvind (2017-04-14). "Semantics derived automatically from language corpora contain human-like biases".

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in an environment so as to maximize some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as

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Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. The data, known as

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Goldwasser, Shafi; Kim, Michael P.; Vaikuntanathan, Vinod; Zamir, Or (14 April 2022). "Planting Undetectable Backdoors in Machine Learning Models".

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uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to

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A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet.

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Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; Vladu, A. (4 September 2019). "Towards deep learning models resistant to adversarial attacks".

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algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros.

9725: 8416: 6164:"Assessing the potential of machine learning methods to study the removal of pharmaceuticals from wastewater using biochar or activated carbon" 5128: 4574: 2541:

specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms.

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set, also called the "number of features". Most of the dimensionality reduction techniques can be considered as either feature elimination or

2064:: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that 10177: 8848: 10016:

M.O.R. Prates; P.H.C. Avelar; L.C. Lamb (11 Mar 2019). "Assessing Gender Bias in Machine Translation – A Case Study with Google Translate".

9992: 9793: 9152: 6502: 4494: 4031:(a particular narrow subdomain of machine learning) that contain many layers of nonlinear hidden units. By 2019, graphic processing units ( 3372:. These belief function approaches that are implemented within the machine learning domain typically leverage a fusion approach of various 2139: 884: 735: 41: 9749:

Pontius, Robert Gilmore; Si, Kangping (2014). "The total operating characteristic to measure diagnostic ability for multiple thresholds".

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Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis. In 2012, co-founder of

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is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as

2818:

Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a

1143: 1108: 466: 8979:"Comparison of a traditional systematic review approach with review-of-reviews and semi-automation as strategies to update the evidence" 12063: 10955: 9337: 7492: 6931: 6212: 5321: 4462: 2474:

Several learning algorithms aim at discovering better representations of the inputs provided during training. Classic examples include

1688:

had come to dominate AI, and statistics was out of favor. Work on symbolic/knowledge-based learning did continue within AI, leading to

1620:", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?". 967: 770: 8016: 5222: 2679:

and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by

1827:

of its points. This process condenses extensive datasets into a more compact set of representative points. Particularly beneficial in

7138: 4467: 10636: 9306: 9272: 9241: 7883:

Ivanenko, Mikhail; Smolik, Waldemar T.; Wanta, Damian; Midura, Mateusz; Wróblewski, Przemysław; Hou, Xiaohan; Yan, Xiaoheng (2023).

7539:." Proceedings of the 7th international joint conference on Artificial intelligence-Volume 2. Morgan Kaufmann Publishers Inc., 1981. 4027:

Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training

3086:) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and 8250: 2224:

Clustering via Large Indel Permuted Slopes, CLIPS, turns the alignment image into a learning regression problem. The varied slope (

1207: 1185: 10879: 10759: 8193: 6609: 5777: 2973:. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. 11249: 7011: 2422:

Self-learning, as a machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning, named

2018: 1457: 1121: 846: 10824: 10541: 9920: 9670: 7859: 5579:

Mentzer, Fabian; Toderici, George; Tschannen, Michael; Agustsson, Eirikur (2020). "High-Fidelity Generative Image Compression".

2917:

Various types of models have been used and researched for machine learning systems, picking the best model for a task is called

1547:, who proposed the early mathematical models of neural networks to come up with algorithms that mirror human thought processes. 12773: 12745: 11132: 10852: 10540:

Louis, Marcia Sahaya; Azad, Zahra; Delshadtehrani, Leila; Gupta, Suyog; Warden, Pete; Reddi, Vijay Janapa; Joshi, Ajay (2019).

10354: 7102: 4035:), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI. 2815:, association rule learning typically does not consider the order of items either within a transaction or across transactions. 2628:

is inspired by a multitude of machine learning methods, starting from supervised learning, reinforcement learning, and finally

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as opposed to software-based approaches. More generally the term is applicable to other artificial neural networks in which a

3830:

Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains. Concern for

12798: 10941: 10926: 10912: 10818: 10753: 10593: 10505: 10380: 9857: 8509: 8214: 7941: 7599: 7583: 7558: 7280: 7201:"Learning efficient haptic shape exploration with a rigid tactile sensor array, S. Fleer, A. Moringen, R. Klatzky, H. Ritter" 6925: 6669: 6548: 6334: 6274: 6138: 6047: 5943:

Mashaghi, A.; Ramezanpour, A. (16 March 2018). "Statistical physics of medical diagnostics: Study of a probabilistic model".

5156: 4789: 4624: 3563: 3090:. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision-making. 1340: 1266: 1220: 1175: 1170: 10415: 6280: 3082:

of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically

12649: 11922: 8228: 4904: 4723:"Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean?" 4430: 3989: 2555:

Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of

2498:. In unsupervised feature learning, features are learned with unlabeled input data. Examples include dictionary learning, 1852:(FLAC) for audio. It achieved compression of image and audio data to 43.4% and 16.4% of their original sizes, respectively. 1617: 904: 667: 202: 11075: 9945: 8877: 8537:"Predicting and Assessing Wildfire Evacuation Decision-Making Using Machine Learning: Findings from the 2019 Kincade Fire" 8502:

Interpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure

7692: 5801:

Hung et al. Algorithms to Measure Surgeon Performance and Anticipate Clinical Outcomes in Robotic Surgery. JAMA Surg. 2018

3938:

meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the

12803: 12082: 12023: 11574: 11311: 9548: 9526: 7713: 4648: 4601:, who coined the term "machine learning" in 1959, but the phrase is not found verbatim in this publication, and may be a 4108: 3847: 3780:

failed to detect a pedestrian, who was killed after a collision. Attempts to use machine learning in healthcare with the

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Characterizing the generalization of various learning algorithms is an active topic of current research, especially for

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that is best sparsely represented by the corresponding dictionary. Sparse dictionary learning has also been applied in

2150: 1502: 1479: 1279: 1148: 1138: 1128: 755: 730: 679: 12955: 12783: 12320: 11835: 11462: 11269: 11125: 11031: 11007: 10989: 10968: 10892: 10793: 10276: 8147:"Application of machine learning to predict visitors' green behavior in marine protected areas: evidence from Cyprus" 7517: 7469: 7045: 6247: 6222: 6018: 5688: 5529: 5449: 5281: 5188: 3971: 3951: 2583:. The key idea is that a clean image patch can be sparsely represented by an image dictionary, but the noise cannot. 1251: 1197: 1163: 1030: 803: 798: 451: 11055: 8535:

Xu, Ningzhe; Lovreglio, Ruggiero; Kuligowski, Erica D.; Cova, Thomas J.; Nilsson, Daniel; Zhao, Xilei (2023-03-01).

7377:

Agrawal, R.; Imieliński, T.; Swami, A. (1993). "Mining association rules between sets of items in large databases".

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often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on

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Michie, D.; Spiegelhalter, D. J.; Taylor, C. C. (1994). "Machine Learning, Neural and Statistical Classification".

6782: 6636: 5176: 5072: 3272: 3241:. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called 2629: 2411: 1729: 1333: 1237: 1083: 461: 99: 17: 10349:

Fafoutis, Xenofon; Marchegiani, Letizia; Elsts, Atis; Pope, James; Piechocki, Robert; Craddock, Ian (2018-05-07).

9406:"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead" 7958: 5625: 1934:

are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population

12431: 10446:"Google, Purdue & Harvard U's Open-Source Framework for TinyML Achieves up to 75x Speedups on FPGAs | Synced" 10131: 7265:, Lecture Notes in Computer Science, vol. 12272, Cham: Springer International Publishing, pp. 462–470, 5871: 2022: 1015: 9453:

Hu, Tongxi; Zhang, Xuesong; Bohrer, Gil; Liu, Yanlan; Zhou, Yuyu; Martin, Jay; LI, Yang; Zhao, Kaiguang (2023).

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Language models learned from data have been shown to contain human-like biases. In an experiment carried out by

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response, then the training error decreases. But if the hypothesis is too complex, then the model is subject to

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Jaiswal, Ashish; Babu, Ashwin Ramesh; Zadeh, Mohammad Zaki; Banerjee, Debapriya; Makedon, Fillia (March 2021).

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interacting with one another set a groundwork for how AIs and machine learning algorithms work under nodes, or

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Dokas, Paul; Ertoz, Levent; Kumar, Vipin; Lazarevic, Aleksandar; Srivastava, Jaideep; Tan, Pang-Ning (2002).

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Ibrahim, Ali; Osta, Mario; Alameh, Mohamad; Saleh, Moustafa; Chible, Hussein; Valle, Maurizio (2019-01-21).

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When A Machine Learning Algorithm Studied Fine Art Paintings, It Saw Things Art Historians Had Never Noticed

6952:

Tillmann, A. M. (2015). "On the Computational Intractability of Exact and Approximate Dictionary Learning".

12945: 12533: 12441: 12353: 12129: 12114: 11952: 11349: 11306: 11259: 11254: 10324: 9367: 8790: 8732: 7981: 7322:"Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets" 7320:

Bassel, George W.; Glaab, Enrico; Marquez, Julietta; Holdsworth, Michael J.; Bacardit, Jaume (2011-09-01).

4990: 4445: 3890: 3674: 3495: 3321: 3313: 3160: 3110: 2475: 2379: 2084:: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as 2010: 1578:

learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.

1469: 788: 725: 635: 613: 456: 446: 11018: 7885:"Image Reconstruction Using Supervised Learning in Wearable Electrical Impedance Tomography of the Thorax" 5181:

The Turing Test Sourcebook: Philosophical and Methodological Issues in the Quest for the Thinking Computer

4671:"Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning" 4246: 2834:. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a 2228:) estimates between each pair of DNA segments enables to identify segments sharing the same set of indels. 1967:

Some statisticians have adopted methods from machine learning, leading to a combined field that they call

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knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously

13033: 12768: 12273: 12003: 11299: 11225: 11106: 8504:, Woodhead Publishing Series in Civil and Structural Engineering, Woodhead Publishing, pp. 185–204, 8497: 8190:"User Interaction Aware Reinforcement Learning for Power and Thermal Efficiency of CPU-GPU Mobile MPSoCs" 7830: 7127: 6807: 4477:

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (

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Jordan, M. I.; Mitchell, T. M. (17 July 2015). "Machine learning: Trends, perspectives, and prospects".

4386: 3155:, where a single line is drawn to best fit the given data according to a mathematical criterion such as 13005: 12654: 11627: 11562: 11163: 10474:

Giri, Davide; Chiu, Kuan-Lin; Di Guglielmo, Giuseppe; Mantovani, Paolo; Carloni, Luca P. (2020-06-15).

9826: 7721: 7628: 6856: 5355: 4558: 4546: 4435: 3928: 3699: 3369: 3346: 3176: 2661: 2550: 2371: 2154: 1570:. It was repetitively "trained" by a human operator/teacher to recognize patterns and equipped with a " 1025: 1008: 899: 826: 576: 471: 259: 192: 152: 31: 8654: 8586: 6700:

Y. Bengio; A. Courville; P. Vincent (2013). "Representation Learning: A Review and New Perspectives".

6352:"Streamline unsupervised machine learning to survey and graph indel-based haplotypes from pan-genomes" 4336: 3917:

Classification of machine learning models can be validated by accuracy estimation techniques like the

2957:

and an arrow represents a connection from the output of one artificial neuron to the input of another.

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Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations

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R. Kohavi and F. Provost, "Glossary of terms", Machine Learning, vol. 30, no. 2–3, pp. 271–274, 1998.

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International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (

4190: 4057: 3931:, which samples n instances with replacement from the dataset, can be used to assess model accuracy. 3689: 3634: 3589: 3584: 3475: 3365: 2966: 2930: 2641: 2520:

algorithms attempt to do so under the constraint that the learned representation is low-dimensional.

2487: 1902: 1849: 1845: 1654: 1575: 1446: 1438: 1396: 1103: 953: 559: 327: 197: 7081: 5428: 5125: 4852: 3005:

would. However, over time, attention moved to performing specific tasks, leading to deviations from

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Although each algorithm has advantages and limitations, no single algorithm works for all problems.

1750: 996: 12874: 12707: 12300: 12169: 11927: 11184: 9192: 8371:"Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates" 7387: 5409:"Measuring the Efficiency of the Intraday Forex Market with a Universal Data Compression Algorithm" 4261: 4064:. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate 4053: 3902: 3815: 3594: 3238: 3218: 2665: 2532:

representations for multidimensional data, without reshaping them into higher-dimensional vectors.

2382:(PCA). PCA involves changing higher-dimensional data (e.g., 3D) to a smaller space (e.g., 2D). The 2367: 2272: 2260: 2198: 1666: 1544: 1519: 1431: 1376: 1227: 581: 501: 424: 342: 172: 134: 129: 89: 84: 9151:

Wang, Xinan; Dasgupta, Sanjoy (2016), Lee, D. D.; Sugiyama, M.; Luxburg, U. V.; Guyon, I. (eds.),

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Sun, Yuran; Zhao, Xilei; Lovreglio, Ruggiero; Kuligowski, Erica (2024-01-01), Naser, M. Z. (ed.),

6630: 3946:(FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The 12935: 12869: 12760: 12576: 12243: 11972: 11957: 11610: 11605: 11505: 11373: 11154: 9981: 8912: 7985: 5554: 4315: 4270: 4049: 3704: 3558: 3542: 2395: 2351: 2041: 1870: 1758: 1757:

There is a close connection between machine learning and compression. A system that predicts the

1670: 1646: 1491: 1372: 988: 528: 377: 277: 104: 9800: 6510: 6417:. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA, USA: 6325:

Jordan, Michael I.; Bishop, Christopher M. (2004). "Neural Networks". In Allen B. Tucker (ed.).

3980:. Systems that are trained on datasets collected with biases may exhibit these biases upon use ( 3784:

system failed to deliver even after years of time and billions of dollars invested. Microsoft's

3279:, or kernel, that models how pairs of points relate to each other depending on their locations. 13000: 12831: 12712: 12479: 12469: 12464: 11932: 11692: 11411: 11406: 7382: 7076: 6806:

Csurka, Gabriella; Dance, Christopher C.; Fan, Lixin; Willamowski, Jutta; Bray, Cédric (2004).

5894:"Statistical Physics for Medical Diagnostics: Learning, Inference, and Optimization Algorithms" 5813:"Breiman: Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)" 5812: 5423: 4847: 4721:

Yoosefzadeh-Najafabadi, Mohsen; Hugh, Earl; Tulpan, Dan; Sulik, John; Eskandari, Milad (2021).

4326: 4116: 3746: 3334: 3222: 3156: 3099: 3049: 2890: 2858: 2827: 2792: 2301: 2111: 2081: 2009:

The computational analysis of machine learning algorithms and their performance is a branch of

1609: 1567: 1098: 708: 684: 586: 347: 322: 282: 94: 10039:"Language necessarily contains human biases, and so will machines trained on language corpora" 8977:

Reddy, Shivani M.; Patel, Sheila; Weyrich, Meghan; Fenton, Joshua; Viswanathan, Meera (2020).

7658: 7379:

Proceedings of the 1993 ACM SIGMOD international conference on Management of data - SIGMOD '93

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An artificial neural network is an interconnected group of nodes, akin to the vast network of

1608:." This definition of the tasks in which machine learning is concerned offers a fundamentally 12970: 12940: 12930: 12826: 12740: 12616: 12556: 12523: 12513: 12403: 12368: 12358: 12295: 12164: 12139: 12134: 12099: 11962: 11947: 11912: 11600: 11500: 11368: 7164: 6531:

van Otterlo, M.; Wiering, M. (2012). "Reinforcement Learning and Markov Decision Processes".

4777: 4104: 4100: 3568: 3455: 3450: 3361: 3287: 3168: 2854: 2831: 2804: 2491: 2278: 2185: 2071: 1935: 1882: 1697: 1650: 1450: 662: 484: 436: 292: 207: 79: 11830: 10952: 9305: 7489: 6915: 3263:

An example of Gaussian Process Regression (prediction) compared with other regression models

3104:

Support-vector machines (SVMs), also known as support-vector networks, are a set of related

2390:, and many dimensionality reduction techniques make this assumption, leading to the area of 1692:(ILP), but the more statistical line of research was now outside the field of AI proper, in 13069: 12730: 12702: 12674: 12669: 12498: 12474: 12426: 12411: 12393: 12383: 12378: 12340: 12290: 12285: 12202: 12148: 11982: 11937: 11383: 11328: 11174: 11169: 10903: 10716:

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

9758: 9101: 8598: 8587:"A machine learning based study on pedestrian movement dynamics under emergency evacuation" 8460: 8012: 7896: 7777: 6971: 6871: 6577: 6175: 6079: 5964: 5214: 5032: 3897: 3807:

is likely to pick up the constitutional and unconscious biases already present in society.

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A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic

3026: 2994: 2507: 2483:, and allows a machine to both learn the features and use them to perform a specific task. 2135: 2127: 2026: 1837: 1770: 1423: 1040: 591: 541: 8281:"Future smartphones 'will prolong their own battery life by monitoring owners' behaviour'" 4401: 4072:

or other electrically adjustable resistance material is used to emulate a neural synapse.

2676: 2605:, a structural defect, medical problems or errors in a text. Anomalies are referred to as 1761:

of a sequence given its entire history can be used for optimal data compression (by using

8: 12995: 12920: 12836: 12821: 12586: 12373: 12330: 12325: 12222: 12212: 12184: 11557: 11535: 11284: 11279: 11237: 11189: 10628: 5385: 5257: 5244: 4803:

Machine learning and pattern recognition "can be viewed as two facets of the same field".

4028: 3967: 3943: 3939: 3604: 3465: 3390: 3276: 3172: 3138: 3105: 3079: 3070: 3018: 2886: 2823: 2800: 2495: 2480: 2383: 2355: 2323: 2282: 2164: 2158: 2101: 2061: 1984: 1980: 1939: 1878: 1769:

An alternative view can show compression algorithms implicitly map strings into implicit

1693: 1563: 1192: 694: 630: 601: 506: 332: 265: 251: 237: 212: 162: 114: 74: 10659:"Machine Learning in Resource-Scarce Embedded Systems, FPGAs, and End-Devices: A Survey" 9762: 9105: 8602: 8464: 8403: 7919: 7900: 7884: 7781: 6975: 6875: 6581: 6179: 6083: 5968: 5863: 5512:(2006). "Compression and Machine Learning: A New Perspective on Feature Space Vectors". 5036: 12960: 12859: 12735: 12692: 12601: 12543: 12528: 12518: 12310: 12109: 11942: 11520: 11064: 10995: 10947: 10871: 10742: 10599: 10546: 10511: 10483: 10386: 10153: 10126: 10102: 10069: 10017: 9912: 9774: 9715:"A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection" 9693: 9579: 9476: 9430: 9405: 9242:"Google 'fixed' its racist algorithm by removing gorillas from its image-labeling tech" 9215: 9133: 9091: 9064: 9013: 8978: 8450: 8439:"Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Methods" 8340: 8220: 8122: 8095: 7812: 7740: 7410: 7354: 7321: 7286: 7237: 7212: 7200: 7181: 7094: 6987: 6961: 6735: 6709: 6601: 6565: 6463: 6422: 6193: 6144: 6103: 6036: 5988: 5954: 5920: 5893: 5834: 5747: 5720: 5580: 5535: 5490: 5467: 5441: 5056: 5044: 4955: 4865: 4816: 4749: 4722: 4700: 4381: 3963: 3871:

The blue line could be an example of overfitting a linear function due to random noise.

3788:

chatbot has been reported to produce hostile and offensive response against its users.

3724: 3669: 3654: 3639: 3629: 3599: 3515: 3419: 3357: 3268: 3254: 3230: 3226: 3014: 2910: 2375: 2339: 2335: 2327: 2202: 2173: 1811: 1737: 1411: 1242: 672: 596: 382: 177: 7831:"Federated Learning: Collaborative Machine Learning without Centralized Training Data" 7037: 7008: 5274:

SUGI 19: proceedings of the Nineteenth Annual SAS Users Group International Conference

4838:

Samuel, Arthur (1959). "Some Studies in Machine Learning Using the Game of Checkers".

13074: 12980: 12910: 12889: 12851: 12659: 12626: 12606: 12305: 12217: 12091: 12008: 11996: 11800: 11452: 11323: 11316: 11027: 11003: 10985: 10964: 10937: 10922: 10908: 10888: 10867: 10849: 10814: 10808: 10789: 10775: 10749: 10723: 10690: 10589: 10515: 10501: 10376: 10158: 10107: 10089: 9916: 9904: 9888: 9660: 9480: 9435: 9375: 9207: 9125: 9117: 9068: 9056: 9018: 9000: 8917: 8674: 8624: 8556: 8505: 8478: 8408: 8390: 8344: 8332: 8224: 8210: 8127: 7924: 7579: 7554: 7513: 7451: 7400: 7359: 7341: 7290: 7276: 7242: 7185: 7041: 7009:

K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

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involves training a model by generating the supervisory signal from the data itself.

2205:. Unsupervised learning algorithms also streamlined the process of identifying large 2115: 2106: 2065: 1943: 1832: 1816: 1762: 1536: 1020: 765: 608: 521: 317: 287: 232: 227: 182: 124: 10603: 10578:

2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS)

10497: 10390: 9778: 9600:"Adversarial Machine Learning – CLTC UC Berkeley Center for Long-Term Cybersecurity" 9471: 9454: 9137: 8206: 8188:

Dey, Somdip; Singh, Amit Kumar; Wang, Xiaohang; McDonald-Maier, Klaus (2020-06-15).

7744: 7609:" Proceedings of the 26th Annual International Conference on Machine Learning, 2009. 7098: 7061: 6991: 5838: 5539: 5445: 40:"Statistical learning" redirects here. For statistical learning in linguistics, see 12697: 12664: 12459: 12388: 12277: 12263: 12258: 12207: 12194: 12119: 12072: 11753: 11743: 11550: 11294: 11289: 11232: 11220: 10680: 10670: 10581: 10493: 10368: 10360: 10351:"Extending the battery lifetime of wearable sensors with embedded machine learning" 10148: 10140: 10097: 10081: 9896: 9766: 9466: 9425: 9417: 9109: 9048: 9008: 8990: 8666: 8614: 8606: 8548: 8468: 8398: 8382: 8324: 8202: 8168: 8158: 8117: 8107: 7914: 7904: 7816: 7804: 7730: 7637: 7441: 7392: 7349: 7333: 7266: 7232: 7222: 7173: 7086: 7033: 6979: 6879: 6719: 6605: 6585: 6536: 6473: 6432: 6393: 6363: 6183: 6126: 6087: 5992: 5972: 5915: 5905: 5824: 5742: 5734: 5600: 5517: 5494: 5482: 5433: 5364: 5252: 5040: 4939: 4869: 4857: 4744: 4734: 4682: 4612: 4241: 4140: 4096: 4084: 4061: 3981: 3801: 3729: 3644: 3547: 3470: 3460: 3407: 3373: 3309: 3234: 3214: 3196: 3164: 3001:

The original goal of the ANN approach was to solve problems in the same way that a

2796: 2580: 2564: 2511: 2469: 2311: 2289: 2191: 2075: 1828: 1791:, AIVC. Examples of software that can perform AI-powered image compression include 1701: 1555: 1506: 1388: 1158: 1093: 793: 546: 496: 406: 390: 360: 222: 217: 167: 157: 10876: 8655:"Modelling and interpreting pre-evacuation decision-making using machine learning" 8610: 8328: 8163: 8146: 7414: 6739: 6589: 6107: 5114:

Duda, R., Hart P. Pattern Recognition and Scene Analysis, Wiley Interscience, 1973

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Hu, Junyan; Niu, Hanlin; Carrasco, Joaquin; Lennox, Barry; Arvin, Farshad (2020).

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in which an electrically adjustable material is used to emulate the function of a

3723:-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an 3074:

discrete set of values are called classification trees; in these tree structures,

1803:'s Image Processing Toolbox (IPT) and High-Fidelity Generative Image Compression. 1732:

it had inherited from AI, and toward methods and models borrowed from statistics,

12884: 12778: 12750: 12644: 12596: 12581: 12566: 12421: 12416: 12363: 12253: 12227: 12179: 12124: 11866: 11810: 11632: 11274: 11194: 11079: 11059: 11044:, IRE Convention Record, Section on Information Theory, Part 2, pp., 56–62, 1957. 11022: 10973: 10959: 10883: 10856: 10629:"dblp: TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning" 9770: 8670: 8044: 7870: 7699: 7606: 7578:. Pearson series in artificial intelligence (Fourth ed.). Hoboken: Pearson. 7536: 7496: 7476: 7271: 7227: 7015: 6883: 6631:

Pavel Brazdil; Christophe Giraud Carrier; Carlos Soares; Ricardo Vilalta (2009).

6436: 6264: 6188: 6163: 5177:"The Annotation Game: On Turing (1950) on Computing, Machinery, and Intelligence" 5132: 5093: 4371: 4251: 4001: 3737: 3659: 3520: 3510: 3505: 3500: 3210: 3087: 3010: 2918: 2812: 2232:

Cluster analysis is the assignment of a set of observations into subsets (called

2114:

is a supervised learning model that divides the data into regions separated by a

2045: 1721: 1713: 1704:

around the same time. This line, too, was continued outside the AI/CS field, as "

1581: 1487: 1415: 1407: 1035: 821: 625: 491: 431: 10476:"ESP4ML: Platform-Based Design of Systems-on-Chip for Embedded Machine Learning" 10364: 8094:

Vaishya, Raju; Javaid, Mohd; Khan, Ibrahim Haleem; Haleem, Abid (July 1, 2020).

6752: 6540: 6478: 6451: 6162:

Okolie, Jude A.; Savage, Shauna; Ogbaga, Chukwuma C.; Gunes, Burcu (June 2022).

6130: 5910: 5652:"AI language models can exceed PNG and FLAC in lossless compression, says study" 4616: 4611:. Artificial Intelligence in Design '96. Springer, Dordrecht. pp. 151–170. 3827:

that learned from Twitter, and it quickly picked up racist and sexist language.

2197:

data. Central applications of unsupervised machine learning include clustering,

12990: 12894: 12793: 12639: 12611: 11840: 11805: 11795: 11620: 11378: 11204: 11072: 11047: 11037: 10898: 10804: 10711: 10675: 10658: 10585: 10573: 10480:

2020 Design, Automation & Test in Europe Conference & Exhibition (DATE)

10475: 10350: 9900: 9518: 9278: 8995: 8552: 8473: 8438: 8195:

2020 Design, Automation & Test in Europe Conference & Exhibition (DATE)

8189: 8145:

Rezapouraghdam, Hamed; Akhshik, Arash; Ramkissoon, Haywantee (March 10, 2021).

8066:"The first AI-generated textbook shows what robot writers are actually good at" 7619: 6368: 6351: 5976: 5891: 5725: 4943: 4376: 4145: 4127: 4083:

is a sub-field of machine learning, where the machine learning model is run on

3977: 3649: 3578: 3490: 3394: 3022: 2873: 2865: 2808: 2775:{\displaystyle \{\mathrm {onions,potatoes} \}\Rightarrow \{\mathrm {burger} \}} 2625: 2556: 2407: 2319: 2307: 2131: 2085: 1942:, while machine learning finds generalizable predictive patterns. According to 1524: 1514: 1368: 841: 372: 109: 10070:"Implementing Machine Learning in Health Care — Addressing Ethical Challenges" 9421: 8112: 7090: 6397: 5437: 5369: 5350: 4640: 3271:

in which every finite collection of the random variables in the process has a

1501:

Although the earliest machine learning model was introduced in the 1950s when

1422:, and medicine. When applied to business problems, it is known under the name 13058: 12879: 12174: 11785: 11765: 11682: 11361: 10977: 10863: 10694: 10093: 9714: 9665: 9379: 9211: 9121: 9060: 9052: 9004: 8921: 8823: 8678: 8628: 8560: 8536: 8482: 8394: 8336: 8285: 7955:"The Netflix Tech Blog: Netflix Recommendations: Beyond the 5 stars (Part 1)" 7455: 7345: 7162:

Chandola, V.; Banerjee, A.; Kumar, V. (2009). "Anomaly detection: A survey".

7004: 6983: 6911: 6487: 6006: 5509: 5172: 5052: 4951: 4739: 4696: 4687: 4670: 4564: 4185: 3820: 3716: 3122: 3066: 3036: 3009:. Artificial neural networks have been used on a variety of tasks, including 2962: 2936: 2680: 2560: 2533: 2521: 2143: 2123: 1961: 1919: 1906: 1788: 1709: 1705: 1685: 1088: 760: 689: 571: 302: 187: 10127:"Implementing Machine Learning in Health Care—Addressing Ethical Challenges" 9113: 7808: 7529: 7177: 6327:

Computer Science Handbook, Second Edition (Section VII: Intelligent Systems)

6091: 5829: 5291: 4597:

The definition "without being explicitly programmed" is often attributed to

2976:

An ANN is a model based on a collection of connected units or nodes called "

12975: 12634: 11871: 11702: 11117: 10779: 10162: 10111: 9908: 9633: 9439: 9129: 9022: 8412: 8131: 7928: 7688: 7430:"Learning Classifier Systems: A Complete Introduction, Review, and Roadmap" 7363: 7337: 7246: 6731: 6597: 6377: 6099: 5984: 5929: 5756: 5716: 5486: 5316: 4758: 4346: 4236: 4130:

containing a variety of machine learning algorithms include the following:

3733: 3720: 3536: 3531: 3184: 3126: 3083: 2877: 2350:. In reinforcement learning, the environment is typically represented as a 1947: 1781: 1717: 1551: 1540: 1392: 1232: 10268: 9577: 8878:"IBM's Watson recommended 'unsafe and incorrect' cancer treatments – STAT" 8585:

Wang, Ke; Shi, Xiupeng; Goh, Algena Pei Xuan; Qian, Shunzhi (2019-06-01).

7446: 7429: 7396: 6723: 5892:

Ramezanpour, A.; Beam, A.L.; Chen, J.H.; Mashaghi, A. (17 November 2020).

5408: 5068: 4291: 12965: 12591: 12503: 11967: 11738: 11647: 11642: 11264: 11242: 11052: 10719: 10144: 10085: 9487: 6509:. referencing work by many other members of Hazy Research. Archived from 5406: 4391: 4361: 4341: 4092: 4011: 3862: 3792:

limiting the necessary sensitivity for the findings research themselves.

3742: 3694: 3485: 3402: 3353: 3002: 2990: 2941: 2598: 2503: 2315: 2306:

Reinforcement learning is an area of machine learning concerned with how

2176:, visual identity tracking, face verification, and speaker verification. 2034: 1957: 1862: 1840:(LLMs) are also capable of lossless data compression, as demonstrated by 1733: 1681: 1613: 1528: 1442: 1419: 1261: 1246: 566: 60: 9219: 8619: 8370: 5521: 5194: 5060: 5020: 3259: 3229:

and learning. Bayesian networks that model sequences of variables, like

3129:, implicitly mapping their inputs into high-dimensional feature spaces. 12985: 12915: 12508: 12248: 12104: 11861: 11820: 11815: 11728: 11637: 11545: 11457: 11437: 8386: 8173: 7735: 7642: 7623: 6535:. Adaptation, Learning, and Optimization. Vol. 12. pp. 3–42. 6121:

El Naqa, Issam; Murphy, Martin J. (2015). "What is Machine Learning?".

5864:

Gareth James; Daniela Witten; Trevor Hastie; Robert Tibshirani (2013).

5738: 4861: 4602: 4416: 4356: 4302: 4256: 4160: 3984:), thus digitizing cultural prejudices. For example, in 1988, the UK's 3835: 3811: 2850: 2602: 2343: 2214: 1931: 1796: 1658: 1427: 1384: 715: 411: 337: 10685: 8948:"How Microsoft's experiment in artificial intelligence tech backfired" 7909: 5865: 3201: 2889:

induction, suggesting a theory to explain observed facts, rather than

2795:, association rules are employed today in application areas including 2528:

algorithms aim to learn low-dimensional representations directly from

1869:

properties learned from the training data, data mining focuses on the

12490: 12451: 11856: 11825: 11723: 11567: 11530: 11467: 11421: 11416: 11401: 9153:"An algorithm for L1 nearest neighbor search via monotonic embedding" 8498:"8 - AI for large-scale evacuation modeling: promises and challenges" 6775:

An analysis of single-layer networks in unsupervised feature learning

6503:"Weak Supervision: The New Programming Paradigm for Machine Learning" 5245:"An Empirical Science Research on Bioinformatics in Machine Learning" 4720: 4406: 4351: 4165: 4150: 4069: 3994: 3785: 3770: 3609: 3573: 3075: 2835: 2568: 2210: 2118:. Here, the linear boundary divides the black circles from the white. 1886: 1645:

As a scientific endeavor, machine learning grew out of the quest for

1510: 1296: 1060: 874: 655: 9661:"Undetectable Backdoors Plantable In Any Machine-Learning Algorithm" 8704:"Why Machine Learning Models Often Fail to Learn: QuickTake Q&A" 6500: 3143: 12551: 12041: 11758: 11590: 10488: 10022: 10015: 9698: 9584: 9096: 8455: 7217: 6468: 6427: 5959: 5585: 5468:"On the Use of Data Compression Measures to Analyze Robust Designs" 5272:

Sarle, Warren S. (1994). "Neural Networks and statistical models".

4552: 4284: 4180: 4175: 3918: 2387: 2386:

proposes that high-dimensional data sets lie along low-dimensional

1841: 1824: 1720:. Their main success came in the mid-1980s with the reinvention of 1399:

have been able to surpass many previous approaches in performance.

1133: 1055: 10178:"Deep Neural Networks for Acoustic Modeling in Speech Recognition" 8826:(Report). Royal United Services Institute (RUSI). pp. 17–22. 8100:

Diabetes & Metabolic Syndrome: Clinical Research & Reviews

7864:

Kathleen DeRose and Christophe Le Lanno (2020). "Machine Learning"

6966: 6714: 5677:"Improving First and Second-Order Methods by Modeling Uncertainty" 5400: 3867: 3058:

A decision tree showing survival probability of passengers on the

1983:. Statistical physics is thus finding applications in the area of 11881: 11718: 11672: 11595: 11495: 11490: 11442: 11114:

is an academic database of open-source machine learning software.

11095: 9599: 8096:"Artificial Intelligence (AI) applications for COVID-19 pandemic" 5679:. In Sra, Suvrit; Nowozin, Sebastian; Wright, Stephen J. (eds.). 5578: 5126:

https://web.cs.umass.edu/publication/docs/1981/UM-CS-1981-028.pdf

4549: – Process of automating the application of machine learning 4279: 4265: 4065: 3934:

In addition to overall accuracy, investigators frequently report

3824: 3712: 3480: 3059: 3006: 2985: 2784: 2606: 2169: 1662: 1301: 650: 27:

Study of algorithms that improve automatically through experience

10657:

Branco, Sérgio; Ferreira, André G.; Cabral, Jorge (2019-11-05).

9691: 8653:

Zhao, Xilei; Lovreglio, Ruggiero; Nilsson, Daniel (2020-05-01).

8311:

Rasekhschaffe, Keywan Christian; Jones, Robert C. (2019-07-01).

7306:, in Piatetsky-Shapiro, Gregory; and Frawley, William J.; eds., 6566:"Nonlinear Dimensionality Reduction by Locally Linear Embedding" 3054: 1905:: Many learning problems are formulated as minimization of some 1787:

Examples of AI-powered audio/video compression software include

1612:

rather than defining the field in cognitive terms. This follows

11896: 11876: 11748: 11540: 11015: 10788:(2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, 10783: 8144: 6699: 6238:

Mohri, Mehryar; Rostamizadeh, Afshin; Talwalkar, Ameet (2012).

4411: 4366: 4320: 4215: 4205: 4200: 4036: 3434: 3430: 3398: 3187:

to implicitly map input variables to higher-dimensional space.

2981: 2946: 2529: 1800: 1792: 1637: 1532: 401: 9629:"Machine-learning models vulnerable to undetectable backdoors" 9193:"Algorithms, Platforms, and Ethnic Bias: An Integrative Essay" 6855:

Lu, Haiping; Plataniotis, K.N.; Venetsanopoulos, A.N. (2011).

6839: 6702:

IEEE Transactions on Pattern Analysis and Machine Intelligence

5501: 4819:(1998). "Data Mining and Statistics: What's the connection?". 4451:

IEEE Transactions on Pattern Analysis and Machine Intelligence

3171:(for example, used for trendline fitting in Microsoft Excel), 3167:. When dealing with non-linear problems, go-to models include 2248:, the difference between clusters. Other methods are based on 11697: 11677: 11667: 11662: 11657: 11652: 11615: 11447: 10574:"Approximate Computing Methods for Embedded Machine Learning" 10473: 10416:"A Beginner's Guide To Machine learning For Embedded Systems" 10208:"GPUs Continue to Dominate the AI Accelerator Market for Now" 10068:

Char, Danton S.; Shah, Nigam H.; Magnus, David (2018-03-15).

9953:

Stanford Institute for Human-Centered Artificial Intelligence

9338:"Microsoft: AI Isn't Yet Adaptable Enough to Help Businesses" 8050: 7018:." Signal Processing, IEEE Transactions on 54 (11): 4311–4322 6753:

Nathan Srebro; Jason D. M. Rennie; Tommi S. Jaakkola (2004).

6414:

Self-Supervised Learning of Pretext-Invariant Representations

5675:

Le Roux, Nicolas; Bengio, Yoshua; Fitzgibbon, Andrew (2012).

4396: 4297: 4210: 4170: 3445:

There are many applications for machine learning, including:

3401:

data, and data collected from individual users of a service.

2970: 2950: 2572: 2244:, or the similarity between members of the same cluster, and 2206: 1559: 645: 640: 367: 10919:

Data Mining: Practical machine learning tools and techniques

10539: 10348: 9549:"AI Has a Hallucination Problem That's Proving Tough to Fix" 8822:

Babuta, Alexander; Oswald, Marion; Rinik, Christine (2018).

7598:

Honglak Lee, Roger Grosse, Rajesh Ranganath, Andrew Y. Ng. "

7319: 7199:

Fleer, S.; Moringen, A.; Klatzky, R. L.; Ritter, H. (2020).

7028:

Zimek, Arthur; Schubert, Erich (2017), "Outlier Detection",

6854: 6815:. ECCV Workshop on Statistical Learning in Computer Vision. 6781:. Int'l Conf. on AI and Statistics (AISTATS). Archived from 6390: 6349: 5626:"Differentially private clustering for large-scale datasets" 4513:

International Conference on Intelligent Robots and Systems (

3183:, which introduces non-linearity by taking advantage of the 2893:, proving a property for all members of a well-ordered set. 2378:. One of the popular methods of dimensionality reduction is 11687: 9858:"Machine learning is racist because the internet is racist" 6857:"A Survey of Multilinear Subspace Learning for Tensor Data" 5942: 4230: 4155: 3777: 1777: 1571: 1550:

By the early 1960s an experimental "learning machine" with

1380: 10325:"Nano-spaghetti to solve neural network power consumption" 8495: 8187: 7198: 6530: 6237: 6005: 5810: 5249:

Journal of Mechanics of Continua and Mathematical Sciences

3393:

of data. Data from the training set can be as varied as a

9751:

International Journal of Geographical Information Science

9722:

International Joint Conference on Artificial Intelligence

9368:"Fei-Fei Li's Quest to Make Machines Better for Humanity" 8534: 7882: 7771: 6805: 6329:. Boca Raton, Florida: Chapman & Hall/CRC Press LLC. 5507: 4107:, optimization of machine learning models and many more. 4032: 1483: 10356:

2018 IEEE 4th World Forum on Internet of Things (WF-IoT)

9499: 9307:"Why Microsoft Accidentally Unleashed a Neo-Nazi Sexbot" 7259: 7125: 6449: 5459: 5407:

Shmilovici A.; Kahiri Y.; Ben-Gal I.; Hauser S. (2009).

5377: 4043: 3879: 2220: 1700:. Neural networks research had been abandoned by AI and 933:

List of datasets in computer vision and image processing

10542:"Towards Deep Learning using TensorFlow Lite on RISC-V" 9889:"AI 'fairness' research held back by lack of diversity" 9273:"Opinion | Artificial Intelligence's White Guy Problem" 9081: 8976: 8791:"9 Reasons why your machine learning project will fail" 8437:

Sun, Yuran; Huang, Shih-Kai; Zhao, Xilei (2024-02-01).

8093: 7135:

Proceedings NSF Workshop on Next Generation Data Mining

6161: 5674: 4991:"History and Evolution of Machine Learning: A Timeline" 4292:

Proprietary software with free and open-source editions

3954:(ROC) and ROC's associated area under the curve (AUC). 2965:

systems, are computing systems vaguely inspired by the

10953:

Information Theory, Inference, and Learning Algorithms

10571: 7376: 4897:"How the Computer Got Its Revenge on the Soviet Union" 4504:

International Conference on Learning Representations (

2864:

Inductive logic programming is particularly useful in

2446:

compute emotion of being in the consequence situation

1460:

provides a framework for describing machine learning.

11111: 7304:

Discovery, analysis, and presentation of strong rules

7161: 5714: 4606: 4531:

Conference on Neural Information Processing Systems (

2689: 2683:(POS) systems in supermarkets. For example, the rule 2537:

factors of variation that explain the observed data.

1877:

properties in the data (this is the analysis step of

10802: 10656: 9160:

Advances in Neural Information Processing Systems 29

8652: 6844:. Pearson Education International. pp. 145–146. 5242: 3766:

people, lack of resources, and evaluation problems.

1445:

is a related (parallel) field of study, focusing on

7428:Urbanowicz, Ryan J.; Moore, Jason H. (2009-09-22). 4928:"The Impact of Automation On Public Administration" 4668: 4522:

Conference on Knowledge Discovery and Data Mining (

3286:Gaussian processes are popular surrogate models in 2354:(MDP). Many reinforcements learning algorithms use 1708:", by researchers from other disciplines including 1437:The mathematical foundations of ML are provided by 10741: 9271: 8905: 8733:"The First Wave of Corporate AI Is Doomed to Fail" 8310: 6772:Coates, Adam; Lee, Honglak; Ng, Andrew Y. (2011). 6452:"A Survey on Contrastive Self-Supervised Learning" 6035: 5386:"Rationale for a Large Text Compression Benchmark" 4570:List of important publications in machine learning 2774: 9849: 8906:Hernandez, Daniela; Greenwald, Ted (2018-08-11). 8849:"Why Uber's self-driving car killed a pedestrian" 8821: 6564:Roweis, Sam T.; Saul, Lawrence K. (22 Dec 2000). 6009:; Rostamizadeh, Afshin; Talwalkar, Ameet (2012). 5772: 2984:in a biological brain. Each connection, like the 13056: 9979: 9818: 9452: 7794: 7434:Journal of Artificial Evolution and Applications 6501:Alex Ratner; Stephen Bach; Paroma Varma; Chris. 6318: 5768: 5766: 5243:Sindhu V, Nivedha S, Prakash M (February 2020). 5236: 5142: 5140: 5105:Nilsson N. Learning Machines, McGraw Hill, 1965. 4133: 3163:methods to mitigate overfitting and bias, as in 2567:and difficult to solve approximately. A popular 2259:A special type of unsupervised learning called, 2149:Types of supervised-learning algorithms include 1780:theory, a connection more directly explained in 1665:that were later found to be reinventions of the 1627: 1554:memory, called Cybertron, had been developed by 11073:Probabilistic Machine Learning: An Introduction 9039:Garcia, Megan (2016). "Racist in the Machine". 8251:"Smartphones get smarter with Essex innovation" 7979: 7774:Ellis Horwood Series in Artificial Intelligence 7711: 7427: 6033: 3757:on pre evacuation decisions in building fires. 3147:Illustration of linear regression on a data set 3078:represent class labels, and branches represent 2822:, with a learning component, performing either 2609:, novelties, noise, deviations and exceptions. 1434:is an important source of the field's methods. 1402:ML finds application in many fields, including 10810:Computational Intelligence: A Logical Approach 10124: 10067: 8584: 8443:International Journal of Disaster Risk Science 7155: 6411:Misra, Ishan; Maaten, Laurens van der (2020). 6069: 4575:List of datasets for machine-learning research 4495:International Conference on Machine Learning ( 928:List of datasets for machine-learning research 12081:Note: This template roughly follows the 2012 12057: 11133: 10963:Cambridge: Cambridge University Press, 2003. 10774: 10239:"AI is changing the entire nature of compute" 10125:Char, D. S.; Shah, N. H.; Magnus, D. (2018). 9162:, Curran Associates, Inc., pp. 983–991, 7712:Goldberg, David E.; Holland, John H. (1988). 7612: 7573: 7128:"Data mining for network intrusion detection" 7062:"A Survey of Outlier Detection Methodologies" 7027: 6910: 6210: 6120: 5763: 5311: 5137: 2924: 2571:method for sparse dictionary learning is the 2544: 1458:probably approximately correct (PAC) learning 1341: 961: 11147: 11063:A privately circulated report from the 1956 9150: 8436: 8010: 7618: 7479:, PhD thesis, University of Edinburgh, 1970. 6809:Visual categorization with bags of keypoints 6324: 5179:, in Epstein, Robert; Peters, Grace (eds.), 5146: 4772: 4770: 4768: 4075: 2844:(ILP) is an approach to rule learning using 2769: 2746: 2740: 2690: 2134:, and the training data is represented by a 1950:as a placeholder to call the overall field. 1375:concerned with the development and study of 42:statistical learning in language acquisition 11016:Artificial Intelligence – A Modern Approach 11013:Stuart Russell & Peter Norvig, (2009). 9946:"Artificial Intelligence Index Report 2021" 9190: 7059: 6771: 6410: 5552: 5344: 5342: 4888: 4555: – Extremely large or complex datasets 4468:Association for Computational Linguistics ( 2362: 2266: 1901:Machine learning also has intimate ties to 12064: 12050: 11140: 11126: 11065:Dartmouth Summer Research Conference on AI 10785:Artificial Intelligence: A Modern Approach 9980:Bostrom, Nick; Yudkowsky, Eliezer (2011). 8368: 7659:"Tutorial: Polynomial Regression in Excel" 7576:Artificial intelligence: a modern approach 7574:Russell, Stuart J.; Norvig, Peter (2021). 7490:Inductive inference of theories from facts 7263:Haptics: Science, Technology, Applications 6765: 6563: 6214:Artificial Intelligence: A Modern Approach 6211:Russell, Stuart J.; Norvig, Peter (2010). 5811:Cornell University Library (August 2001). 5465: 5351:"The changing science of machine learning" 5322:Artificial Intelligence: A Modern Approach 4463:AAAI Conference on Artificial Intelligence 4015:will necessarily also learn these biases. 3093: 1632: 1348: 1334: 968: 954: 10684: 10674: 10487: 10373:1983/b8fdb58b-7114-45c6-82e4-4ab239c1327f 10152: 10101: 10036: 10021: 9748: 9697: 9583: 9470: 9429: 9095: 9012: 8994: 8762:"Why the A.I. euphoria is doomed to fail" 8618: 8472: 8454: 8402: 8369:Chung, Yunsie; Green, William H. (2024). 8172: 8162: 8121: 8111: 7918: 7908: 7734: 7714:"Genetic algorithms and machine learning" 7641: 7445: 7386: 7353: 7296: 7270: 7236: 7226: 7216: 7080: 6965: 6840:Daniel Jurafsky; James H. Martin (2009). 6713: 6633:Metalearning: Applications to Data Mining 6477: 6467: 6426: 6367: 6187: 5958: 5919: 5909: 5828: 5746: 5584: 5427: 5368: 5256: 4894: 4851: 4765: 4748: 4738: 4686: 4675:IEEE Transactions on Vehicular Technology 4087:with limited computing resources such as 3424:Federated learning is an adapted form of 2953:. Here, each circular node represents an 2295: 10744:Artificial Intelligence: A New Synthesis 10710: 10175: 9505: 9493: 9335: 9269: 8278: 7470:Automatic Methods of Inductive Inference 6951: 6833: 6262: 6027: 5339: 4926:Lindsay, Richard P. (1 September 1964). 4815: 4782:Pattern Recognition and Machine Learning 3866: 3258: 3200: 3142: 3053: 2940: 2281:(without any labeled training data) and 2219: 2179: 2105: 1636: 1566:, and speech patterns using rudimentary 11000:Neural Networks for Pattern Recognition 10736: 9982:"THE ETHICS OF ARTIFICIAL INTELLIGENCE" 9939: 9937: 9794:"The Ethics of Artificial Intelligence" 9791: 9519:"Single pixel change fools AI programs" 9239: 9186: 9184: 9182: 9034: 9032: 8817: 8815: 8063: 5867:An Introduction to Statistical Learning 5649: 5348: 5307: 5305: 5303: 5301: 4925: 4840:IBM Journal of Research and Development 4308: 2853:all positive and no negative examples. 2277:Semi-supervised learning falls between 2019:Probably Approximately Correct Learning 1680:However, an increasing emphasis on the 1426:. Although not all machine learning is 14: 13057: 12774:Knowledge representation and reasoning 11107:International Machine Learning Society 10443: 9712: 9365: 9191:Silva, Selena; Kenney, Martin (2018). 9038: 8313:"Machine Learning for Stock Selection" 7551:The hundred-page machine learning book 7548: 7370: 6302:"Lecture 2 Notes: Supervised Learning" 6123:Machine Learning in Radiation Oncology 5601:"What is Unsupervised Learning? | IBM" 5171: 5018: 4985: 4983: 4981: 4837: 4776: 3132: 2961:Artificial neural networks (ANNs), or 2671:Based on the concept of strong rules, 2095: 2025:is one way to quantify generalization 1974: 12799:Philosophy of artificial intelligence 12045: 11121: 10917:Ian H. Witten and Eibe Frank (2011). 10813:. New York: Oxford University Press. 10037:Narayanan, Arvind (August 24, 2016). 9868:from the original on 17 November 2017 9837:from the original on 17 November 2017 9827:"The fight against racist algorithms" 9571: 9403: 8958:from the original on December 8, 2023 8945: 7656: 5271: 4831: 4609:Artificial Intelligence in Design '96 4044:Neuromorphic/Physical Neural Networks 3880:Other limitations and vulnerabilities 3711:In 2006, the media-services provider 3413: 3316:technique that mimics the process of 3297: 3248: 2838:manner in order to make predictions. 1527:formed by certain interactions among 1486:employee and pioneer in the field of 12125:Energy consumption (Green computing) 12071: 11978:Generative adversarial network (GAN) 10877:The Elements of Statistical Learning 10305:from the original on 27 October 2021 10188:from the original on 1 February 2016 9934: 9886: 9855: 9303: 9179: 9029: 8812: 7858:Machine learning is included in the 6920:. Now Publishers Inc. pp. 1–3. 6693: 5936: 5885: 5857: 5721:"Statistics versus Machine Learning" 5514:Data Compression Conference (DCC'06) 5298: 4881: 4879: 4811: 4809: 4716: 4714: 4431:Journal of Machine Learning Research 3912: 3190: 2635: 2586: 1649:(AI). In the early days of AI as an 1618:Computing Machinery and Intelligence 1588:with respect to some class of tasks 1441:(mathematical programming) methods. 12804:Distributed artificial intelligence 12083:ACM Computing Classification System 10236: 9824: 9459:Agricultural and Forest Meteorology 8248: 8013:"Do We Need Doctors or Algorithms?" 7797:Computational Intelligence Magazine 7302:Piatetsky-Shapiro, Gregory (1991), 7032:, Springer New York, pp. 1–5, 7007:, M Elad, and A Bruckstein. 2006. " 6755:Maximum-Margin Matrix Factorization 6612:from the original on 15 August 2021 5383: 5276:. SAS Institute. pp. 1538–50. 4978: 4966:from the original on 6 October 2021 3848:Explainable artificial intelligence 3426:distributed artificial intelligence 3340: 2463: 2037:and generalization will be poorer. 1751:Data compression § Machine learning 1743: 1498:was also used in this time period. 923:Glossary of artificial intelligence 24: 12316:Integrated development environment 10840: 10049:from the original on June 25, 2018 9559:from the original on 12 March 2018 9529:from the original on 22 March 2018 9229:from the original on Jan 27, 2024. 8019:from the original on June 18, 2018 7021: 6917:Learning Deep Architectures for AI 6256: 5999: 5804: 5258:10.26782/jmcms.spl.7/2020.02.00006 5045:10.1038/scientificamerican0193-124 4662: 4633: 3896:Researchers have demonstrated how 3383: 3159:. The latter is often extended by 2765: 2762: 2759: 2756: 2753: 2750: 2736: 2733: 2730: 2727: 2724: 2721: 2718: 2715: 2709: 2706: 2703: 2700: 2697: 2694: 1641:Machine learning as subfield of AI 995: 25: 13086: 12784:Automated planning and scheduling 12321:Software configuration management 11088: 10827:from the original on 26 July 2020 10762:from the original on 26 July 2020 10279:from the original on 17 June 2020 10218:from the original on 10 June 2020 9943: 8279:Williams, Rhiannon (2020-07-21). 8011:Vinod Khosla (January 10, 2012). 7992:from the original on 24 June 2018 7060:Hodge, V. J.; Austin, J. (2004). 7038:10.1007/978-1-4899-7993-3_80719-1 6217:(Third ed.). Prentice Hall. 6168:Total Environment Research Themes 5845:from the original on 26 June 2017 5778:"statistics and machine learning" 5681:Optimization for Machine Learning 5388:. Florida Institute of Technology 4895:Gerovitch, Slava (9 April 2015). 4876: 4806: 4711: 3976:Machine learning poses a host of 3972:Ethics of artificial intelligence 3952:receiver operating characteristic 3841: 3776:In 2018, a self-driving car from 3275:, and it relies on a pre-defined 3043: 2664:, association rule learning, and 2620: 1913: 1682:logical, knowledge-based approach 1673:was also employed, especially in 13038: 13028: 13019: 13018: 12016: 12015: 11995: 11094: 10984:(2nd edition), Wiley, New York, 10934:Introduction to Machine Learning 10850:Introduction to Machine Learning 10650: 10621: 10565: 10533: 10467: 10437: 10408: 10342: 10317: 10291: 10261: 10249:from the original on 25 May 2020 10230: 10200: 10176:Research, AI (23 October 2015). 10169: 10118: 10061: 10030: 10009: 9973: 9880: 9785: 9742: 9706: 9685: 9673:from the original on 11 May 2022 9653: 9641:from the original on 13 May 2022 9621: 9592: 9541: 9511: 9446: 9397: 9359: 9336:Simonite, Tom (March 30, 2017). 9329: 9297: 9263: 9233: 9144: 9075: 8970: 8939: 8899: 8870: 8841: 8824:Transparency and Intelligibility 8783: 8754: 8725: 8696: 8646: 8578: 8528: 8489: 8430: 8362: 8304: 8272: 8242: 8181: 8138: 8087: 8057: 8031: 8004: 7980:Scott Patterson (13 July 2010). 7973: 7669:from the original on 2 June 2013 7310:, AAAI/MIT Press, Cambridge, MA. 7308:Knowledge Discovery in Databases 7030:Encyclopedia of Database Systems 6266:Introduction to Machine Learning 6038:Introduction to Machine Learning 6013:. US, Massachusetts: MIT Press. 5475:IEEE Transactions on Reliability 5455:from the original on 2009-07-09. 4821:Computing Science and Statistics 4387:Oracle AI Platform Cloud Service 3905:is provided, possibly including 3273:multivariate normal distribution 2440:receive a consequence situation 2417: 1749:This section is an excerpt from 1387:to unseen data and thus perform 13029: 12432:Computational complexity theory 10639:from the original on 2022-01-18 10610:from the original on 2022-01-17 10554:from the original on 2022-01-17 10522:from the original on 2022-01-18 10498:10.23919/DATE48585.2020.9116317 10456:from the original on 2022-01-18 10426:from the original on 2022-01-18 10397:from the original on 2022-01-18 10331:from the original on 2021-10-06 10132:New England Journal of Medicine 10074:New England Journal of Medicine 9998:from the original on 2015-12-20 9962:from the original on 2024-05-19 9923:from the original on 2023-04-12 9731:from the original on 2018-07-12 9610:from the original on 2022-05-17 9472:10.1016/j.agrformet.2023.109458 9386:from the original on 2020-12-14 9348:from the original on 2018-11-09 9318:from the original on 2018-11-09 9304:Metz, Rachel (March 24, 2016). 9286:from the original on 2021-01-14 9270:Crawford, Kate (25 June 2016). 9252:from the original on 2018-08-21 9240:Vincent, James (Jan 12, 2018). 9169:from the original on 2017-04-07 8928:from the original on 2018-08-21 8888:from the original on 2018-08-21 8859:from the original on 2018-08-21 8830:from the original on 2023-12-09 8801:from the original on 2018-08-21 8772:from the original on 2018-08-19 8743:from the original on 2018-08-21 8685:from the original on 2024-05-19 8635:from the original on 2024-05-19 8567:from the original on 2024-05-19 8518:from the original on 2024-05-19 8419:from the original on 2024-05-19 8351:from the original on 2023-11-26 8293:from the original on 2021-06-24 8261:from the original on 2021-06-24 8231:from the original on 2021-12-13 8207:10.23919/DATE48585.2020.9116294 8076:from the original on 2019-05-05 7947: 7935: 7876: 7862:(discussion is top-down); see: 7852: 7841:from the original on 2019-06-07 7823: 7788: 7765: 7754:from the original on 2011-05-16 7705: 7681: 7650: 7592: 7567: 7542: 7522: 7502: 7482: 7462: 7421: 7313: 7253: 7192: 7144:from the original on 2015-09-23 7119: 7108:from the original on 2015-06-22 7053: 6998: 6945: 6934:from the original on 2023-01-17 6904: 6893:from the original on 2019-07-10 6848: 6822:from the original on 2019-07-13 6799: 6746: 6684: 6675: 6657: 6637:Springer Science+Business Media 6624: 6557: 6524: 6494: 6443: 6404: 6384: 6343: 6294: 6283:from the original on 2023-01-17 6240:Foundations of Machine Learning 6231: 6204: 6155: 6114: 6063: 6011:Foundations of Machine Learning 5874:from the original on 2019-06-23 5795: 5784:from the original on 2017-10-18 5708: 5697:from the original on 2023-01-17 5668: 5643: 5618: 5593: 5572: 5555:"What Is AI Video Compression?" 5553:Gary Adcock (January 5, 2023). 5546: 5325:(2nd ed.). Prentice Hall. 5265: 5225:from the original on 2021-02-18 5207: 5165: 5117: 5108: 5099: 5086: 5075:from the original on 2023-12-20 5012: 5001:from the original on 2023-12-08 4919: 4651:from the original on 2023-12-27 3440: 3376:to better handle the learner's 2652:Association rule learning is a 2240:and evaluated, for example, by 1596:if its performance at tasks in 1016:Artificial general intelligence 12223:Network performance evaluation 11928:Recurrent neural network (RNN) 11918:Differentiable neural computer 11053:An Inductive Inference Machine 11042:An Inductive Inference Machine 8151:Journal of Sustainable Tourism 7622:; Vapnik, Vladimir N. (1995). 7512:. Cambridge, Mass: MIT Press. 7069:Artificial Intelligence Review 6954:IEEE Signal Processing Letters 6842:Speech and Language Processing 4797: 4591: 4456: 3986:Commission for Racial Equality 3948:total operating characteristic 3856: 3760: 3625:Natural language understanding 3362:imprecise probability theories 3308:A genetic algorithm (GA) is a 3065:Decision tree learning uses a 2743: 2500:independent component analysis 2401: 2130:or vector, sometimes called a 1856: 1456:From a theoretical viewpoint, 343:Relevance vector machine (RVM) 13: 1: 12587:Multimedia information system 12572:Geographic information system 12562:Enterprise information system 12158:Computer systems organization 11973:Variational autoencoder (VAE) 11933:Long short-term memory (LSTM) 11200:Computational learning theory 8946:Allyn, Bobby (Feb 27, 2023). 8611:10.1016/j.firesaf.2019.04.008 8329:10.1080/0015198X.2019.1596678 8164:10.1080/09669582.2021.1887878 8064:Vincent, James (2019-04-10). 7982:"Letting the Machines Decide" 7510:Algorithmic program debugging 6590:10.1126/science.290.5500.2323 5719:; Krzywinski, Martin (2018). 5021:"The Mind and Donald O. Hebb" 4585: 4580:M-theory (learning framework) 4134:Free and open-source software 3680:Syntactic pattern recognition 3027:playing board and video games 2526:Multilinear subspace learning 2332:simulation-based optimization 2051: 2015:computational learning theory 1997:Computational learning theory 1925: 1808:unsupervised machine learning 1628:Relationships to other fields 832:Computational learning theory 396:Expectation–maximization (EM) 12946:Computational social science 12534:Theoretical computer science 12354:Software development process 12130:Electronic design automation 12115:Very Large Scale Integration 11953:Convolutional neural network 9887:Wong, Carissa (2023-03-30). 9771:10.1080/13658816.2013.862623 9366:Hempel, Jessi (2018-11-13). 8710:. 2016-11-10. Archived from 8671:10.1016/j.autcon.2020.103140 7957:. 2012-04-06. Archived from 7272:10.1007/978-3-030-58147-3_51 7228:10.1371/journal.pone.0226880 6884:10.1016/j.patcog.2011.01.004 6437:10.1109/CVPR42600.2020.00674 6189:10.1016/j.totert.2022.100001 5650:Edwards, Benj (2023-09-28). 5092:"Science: The Goof Button", 4561: – Programming paradigm 3891:adversarial machine learning 3675:Structural health monitoring 2476:principal component analysis 2380:principal component analysis 2011:theoretical computer science 1604:, improves with experience 1525:theoretical neural structure 1520:The Organization of Behavior 1470:Timeline of machine learning 789:Coefficient of determination 636:Convolutional neural network 348:Support vector machine (SVM) 7: 12769:Natural language processing 12557:Information storage systems 11948:Multilayer perceptron (MLP) 11002:, Oxford University Press. 10365:10.1109/WF-IoT.2018.8355116 9410:Nature Machine Intelligence 6541:10.1007/978-3-642-27645-3_1 6479:10.3390/technologies9010002 6131:10.1007/978-3-319-18305-3_1 5911:10.3390/diagnostics10110972 5215:"Introduction to AI Part 1" 4932:Western Political Quarterly 4641:"What is Machine Learning?" 4617:10.1007/978-94-009-0279-4_9 4540: 4441:Nature Machine Intelligence 4424: 4196:Microsoft Cognitive Toolkit 4122: 4022: 3990:St. George's Medical School 3936:sensitivity and specificity 3620:Natural language processing 3292:hyperparameter optimization 3121:, although methods such as 2980:", which loosely model the 2870:natural language processing 2842:Inductive logic programming 2662:learning classifier systems 2654:rule-based machine learning 2648:Inductive logic programming 2213:of a gene of interest from 2023:bias–variance decomposition 2001:Statistical learning theory 1690:inductive logic programming 1675:automated medical diagnosis 1523:, in which he introduced a 1404:natural language processing 1051:Natural language processing 940:Outline of machine learning 837:Empirical risk minimization 10: 13091: 12685:Human–computer interaction 12655:Intrusion detection system 12567:Social information systems 12552:Database management system 12024:Artificial neural networks 11938:Gated recurrent unit (GRU) 11164:Differentiable programming 10704: 10676:10.3390/electronics8111289 10586:10.1109/ICECS.2018.8617877 9901:10.1038/d41586-023-00935-z 8996:10.1186/s13643-020-01450-2 8908:"IBM Has a Watson Dilemma" 8659:Automation in Construction 8553:10.1007/s10694-023-01363-1 8474:10.1007/s13753-024-00541-1 8317:Financial Analysts Journal 7530:The model inference system 6369:10.1016/j.molp.2023.05.005 5977:10.1103/PhysRevE.97.032118 5683:. MIT Press. p. 404. 5183:, Kluwer, pp. 23–66, 5151:. McGraw Hill. p. 2. 4944:10.1177/106591296401700364 4559:Differentiable programming 4547:Automated machine learning 3961: 3903:data/software transparency 3860: 3845: 3799: 3700:Tomographic reconstruction 3417: 3397:, a collection of images, 3370:uncertainty quantification 3344: 3301: 3252: 3194: 3177:statistical classification 3136: 3097: 3047: 2967:biological neural networks 2934: 2928: 2925:Artificial neural networks 2645: 2639: 2590: 2551:Sparse dictionary learning 2548: 2545:Sparse dictionary learning 2488:artificial neural networks 2467: 2299: 2290:weakly supervised learning 2270: 2189: 2183: 2099: 1994: 1748: 1616:'s proposal in his paper " 1467: 1463: 1397:artificial neural networks 1104:Hybrid intelligent systems 1026:Recursive self-improvement 577:Feedforward neural network 328:Artificial neural networks 39: 29: 13014: 12951:Computational engineering 12926:Computational mathematics 12903: 12850: 12812: 12759: 12721: 12683: 12625: 12542: 12488: 12450: 12402: 12339: 12272: 12236: 12193: 12157: 12090: 12079: 11991: 11905: 11849: 11778: 11711: 11583: 11483: 11476: 11430: 11394: 11357:Artificial neural network 11337: 11213: 11180:Automatic differentiation 11153: 10921:Morgan Kaufmann, 664pp., 9422:10.1038/s42256-019-0048-x 8113:10.1016/j.dsx.2020.04.012 7663:facultystaff.richmond.edu 7624:"Support-vector networks" 7508:Shapiro, Ehud Y. (1983). 7091:10.1007/s10462-004-4304-y 6398:10.1101/2023.02.11.527743 6042:. London: The MIT Press. 5870:. Springer. p. vii. 5438:10.1007/s10614-008-9153-3 5370:10.1007/s10994-011-5242-y 5019:Milner, Peter M. (1993). 4081:Embedded Machine Learning 4076:Embedded Machine Learning 4058:artificial neural network 3957: 3585:Knowledge graph embedding 3476:Automated decision-making 3239:dynamic Bayesian networks 3213:that represents a set of 2931:Artificial neural network 2896: 2666:artificial immune systems 2642:Association rule learning 2044:. There are two kinds of 1990: 1850:Free Lossless Audio Codec 1846:Portable Network Graphics 1667:generalized linear models 1576:artificial neural network 1447:exploratory data analysis 1439:mathematical optimization 560:Artificial neural network 12961:Computational healthcare 12956:Differentiable computing 12875:Graphics processing unit 12301:Domain-specific language 12170:Computational complexity 11185:Neuromorphic engineering 11148:Differentiable computing 11070:Kevin P. Murphy (2021). 10980:, David G. Stork (2001) 10807:; Goebel, Randy (1998). 10420:Analytics India Magazine 9053:10.1215/07402775-3813015 7657:Stevenson, Christopher. 7553:. Polen: Andriy Burkov. 6984:10.1109/LSP.2014.2345761 6269:. MIT Press. p. 9. 6263:Alpaydin, Ethem (2010). 6034:Alpaydin, Ethem (2010). 4740:10.3389/fpls.2020.624273 4688:10.1109/tvt.2020.3034800 3816:investigative journalism 3595:Machine learning control 3496:Brain–machine interfaces 3320:, using methods such as 3267:A Gaussian process is a 3219:conditional independence 2454:w'(a,s) = w(a,s) + v(s') 2452:update crossbar memory 2368:Dimensionality reduction 2363:Dimensionality reduction 2273:Semi-supervised learning 2267:Semi-supervised learning 2261:self-supervised learning 2199:dimensionality reduction 1592:and performance measure 1432:computational statistics 1228:Artificial consciousness 869:Journals and conferences 816:Mathematical foundations 726:Temporal difference (TD) 582:Recurrent neural network 502:Conditional random field 425:Dimensionality reduction 173:Dimensionality reduction 135:Quantum machine learning 130:Neuromorphic engineering 90:Self-supervised learning 85:Semi-supervised learning 12936:Computational chemistry 12870:Photograph manipulation 12761:Artificial intelligence 12577:Decision support system 11958:Residual neural network 11374:Artificial Intelligence 10932:Ethem Alpaydin (2004). 9496:, Chapter 6, Chapter 7. 9404:Rudin, Cynthia (2019). 9114:10.1126/science.aal4230 8913:The Wall Street Journal 8737:Harvard Business Review 7986:The Wall Street Journal 7809:10.1109/mci.2011.942584 7549:Burkov, Andriy (2019). 7178:10.1145/1541880.1541882 6092:10.1126/science.aaa8415 5416:Computational Economics 4316:Amazon Machine Learning 4050:physical neural network 3795: 3705:User behavior analytics 3695:Time-series forecasting 3559:Handwriting recognition 3335:evolutionary algorithms 3094:Support-vector machines 2969:that constitute animal 2424:crossbar adaptive array 2396:manifold regularization 2352:Markov decision process 1759:posterior probabilities 1671:Probabilistic reasoning 1647:artificial intelligence 1633:Artificial intelligence 1496:self-teaching computers 1492:artificial intelligence 1373:artificial intelligence 1099:Evolutionary algorithms 989:Artificial intelligence 278:Apprenticeship learning 13001:Educational technology 12832:Reinforcement learning 12582:Process control system 12480:Computational geometry 12470:Algorithmic efficiency 12465:Analysis of algorithms 12120:Systems on Chip (SoCs) 11099:Quotations related to 10982:Pattern classification 10714:(September 22, 2015). 10482:. pp. 1049–1054. 9792:Bostrom, Nick (2011). 8201:. pp. 1728–1733. 7687:The documentation for 7338:10.1105/tpc.111.088153 6533:Reinforcement Learning 6507:hazyresearch.github.io 6421:. pp. 6707–6717. 5487:10.1109/TR.2005.853280 4337:Google Cloud Vertex AI 4327:Azure Machine Learning 4117:Knowledge Distillation 3872: 3347:Dempster–Shafer theory 3264: 3223:directed acyclic graph 3206: 3157:ordinary least squares 3148: 3100:Support-vector machine 3062: 3050:Decision tree learning 2958: 2905:machine learning model 2891:mathematical induction 2828:reinforcement learning 2793:market basket analysis 2776: 2492:multilayer perceptrons 2302:Reinforcement learning 2296:Reinforcement learning 2229: 2174:recommendation systems 2140:iterative optimization 2119: 2112:support-vector machine 2082:Reinforcement learning 1642: 1610:operational definition 1568:reinforcement learning 1478:was coined in 1959 by 1377:statistical algorithms 1000: 827:Bias–variance tradeoff 709:Reinforcement learning 685:Spiking neural network 95:Reinforcement learning 12971:Electronic publishing 12941:Computational biology 12931:Computational physics 12827:Unsupervised learning 12741:Distributed computing 12617:Information retrieval 12524:Mathematical analysis 12514:Mathematical software 12404:Theory of computation 12369:Software construction 12359:Requirements analysis 12237:Software organization 12165:Computer architecture 12135:Hardware acceleration 12100:Printed circuit board 11913:Neural Turing machine 11501:Human image synthesis 10444:Synced (2022-01-12). 10237:Ray, Tiernan (2019). 9342:MIT Technology Review 9312:MIT Technology Review 8952:National Public Radio 7397:10.1145/170035.170072 7165:ACM Computing Surveys 6724:10.1109/tpami.2013.50 5830:10.1214/ss/1009213726 5349:Langley, Pat (2011). 5147:Mitchell, T. (1997). 4647:. 22 September 2021. 4342:Google Prediction API 4105:approximate computing 4101:hardware acceleration 4054:Neuromorphic computer 3942:(FPR) as well as the 3870: 3569:Information retrieval 3288:Bayesian optimization 3262: 3204: 3169:polynomial regression 3146: 3057: 2944: 2855:Inductive programming 2832:unsupervised learning 2805:continuous production 2777: 2510:and various forms of 2279:unsupervised learning 2223: 2186:Unsupervised learning 2180:Unsupervised learning 2109: 2072:Unsupervised learning 1930:Machine learning and 1883:unsupervised learning 1861:Machine learning and 1848:(PNG) for images and 1838:Large language models 1771:feature space vectors 1698:information retrieval 1657:"; these were mostly 1640: 1451:unsupervised learning 999: 663:Neural radiance field 485:Structured prediction 208:Structured prediction 80:Unsupervised learning 30:For the journal, see 12731:Concurrent computing 12703:Ubiquitous computing 12675:Application security 12670:Information security 12499:Discrete mathematics 12475:Randomized algorithm 12427:Computability theory 12412:Model of computation 12384:Software maintenance 12379:Software engineering 12341:Software development 12291:Programming language 12286:Programming paradigm 12203:Network architecture 12004:Computer programming 11983:Graph neural network 11558:Text-to-video models 11536:Text-to-image models 11384:Large language model 11369:Scientific computing 11175:Statistical manifold 11170:Information geometry 10904:The Master Algorithm 10580:. pp. 845–848. 10359:. pp. 269–274. 10145:10.1056/nejmp1714229 10086:10.1056/NEJMp1714229 9856:Jeffries, Adrianne. 9713:Kohavi, Ron (1995). 9041:World Policy Journal 5630:blog.research.google 4907:on 22 September 2021 4402:SAS Enterprise Miner 4309:Proprietary software 4029:deep neural networks 3665:Software engineering 3554:General game playing 2687: 2559:and assumed to be a 2508:matrix factorization 2242:internal compactness 1981:deep neural networks 1969:statistical learning 1424:predictive analytics 1379:that can learn from 1041:General game playing 852:Statistical learning 750:Learning with humans 542:Local outlier factor 13006:Document management 12996:Operations research 12921:Enterprise software 12837:Multi-task learning 12822:Supervised learning 12544:Information systems 12374:Software deployment 12331:Software repository 12185:Real-time computing 11350:In-context learning 11190:Pattern recognition 10748:. Morgan Kaufmann. 9944:Zhang, Jack Clark. 9825:Edionwe, Tolulope. 9763:2014IJGIS..28..570P 9525:. 3 November 2017. 9206:(1 & 2): 9–37. 9106:2017Sci...356..183C 8603:2019FirSJ.106..163W 8591:Fire Safety Journal 8465:2024IJDRS..15..134S 7901:2023Senso..23.7774I 7782:1994mlns.book.....M 7447:10.1155/2009/736398 6976:2015ISPL...22...45T 6876:2011PatRe..44.1540L 6864:Pattern Recognition 6635:(Fourth ed.). 6582:2000Sci...290.2323R 6576:(5500): 2323–2326. 6180:2022TERT....100001O 6084:2015Sci...349..255J 5969:2018PhRvE..97c2118M 5817:Statistical Science 5607:. 23 September 2021 5522:10.1109/DCC.2006.13 5466:I. Ben-Gal (2008). 5037:1993SciAm.268a.124M 5025:Scientific American 4817:Friedman, Jerome H. 4681:(12): 14413–14423. 3968:Toronto Declaration 3944:false negative rate 3940:false positive rate 3640:Recommender systems 3605:Machine translation 3466:Affective computing 3277:covariance function 3173:logistic regression 3139:Regression analysis 3133:Regression analysis 3106:supervised learning 3019:machine translation 2859:functional programs 2824:supervised learning 2811:. In contrast with 2801:intrusion detection 2496:dictionary learning 2481:feature engineering 2384:manifold hypothesis 2356:dynamic programming 2336:multi-agent systems 2324:operations research 2283:supervised learning 2165:Similarity learning 2102:Supervised learning 2096:Supervised learning 2062:Supervised learning 1985:medical diagnostics 1975:Statistical physics 1879:knowledge discovery 1730:symbolic approaches 1694:pattern recognition 1651:academic discipline 1517:published the book 1193:Machine translation 1109:Systems integration 1046:Knowledge reasoning 983:Part of a series on 695:Electrochemical RAM 602:reservoir computing 333:Logistic regression 252:Supervised learning 238:Multimodal learning 213:Feature engineering 158:Generative modeling 120:Rule-based learning 115:Curriculum learning 75:Supervised learning 50:Part of a series on 12789:Search methodology 12736:Parallel computing 12693:Interaction design 12602:Computing platform 12529:Numerical analysis 12519:Information theory 12311:Software framework 12274:Software notations 12213:Network components 12110:Integrated circuit 11943:Echo state network 11831:Jürgen Schmidhuber 11526:Facial recognition 11521:Speech recognition 11431:Software libraries 11078:2021-04-11 at the 11058:2011-04-26 at the 11021:2011-02-28 at the 10996:Christopher Bishop 10958:2016-02-17 at the 10948:David J. C. MacKay 10901:(September 2015), 10882:2013-10-27 at the 10872:Jerome H. Friedman 10855:2019-08-16 at the 10776:Russell, Stuart J. 10547:Harvard University 8983:Systematic Reviews 8387:10.1039/D3SC05353A 8043:2016-06-04 at the 7942:"BelKor Home Page" 7869:2020-01-13 at the 7736:10.1007/bf00113892 7698:2022-11-02 at the 7643:10.1007/BF00994018 7605:2017-10-18 at the 7535:2023-04-06 at the 7528:Shapiro, Ehud Y. " 7495:2021-08-21 at the 7475:2017-12-22 at the 7014:2018-11-23 at the 6639:. pp. 10–14, 6306:www.cs.cornell.edu 5739:10.1038/nmeth.4642 5131:2021-02-25 at the 4862:10.1147/rd.33.0210 4446:Neural Computation 4382:Oracle Data Mining 4089:wearable computers 4002:collection of data 4000:While responsible 3964:AI control problem 3873: 3670:Speech recognition 3655:Sentiment analysis 3630:Online advertising 3600:Machine perception 3420:Federated learning 3414:Federated learning 3298:Genetic algorithms 3269:stochastic process 3265: 3255:Gaussian processes 3249:Gaussian processes 3243:influence diagrams 3207: 3149: 3063: 3015:speech recognition 2978:artificial neurons 2959: 2911:mathematical model 2789:product placements 2772: 2348:genetic algorithms 2340:swarm intelligence 2328:information theory 2254:graph connectivity 2230: 2203:density estimation 2144:objective function 2120: 2068:inputs to outputs. 1910:set of examples). 1812:k-means clustering 1738:probability theory 1643: 1564:electrocardiograms 1537:artificial neurons 1531:. Hebb's model of 1412:speech recognition 1001: 263: • 178:Density estimation 13052: 13051: 12981:Electronic voting 12911:Quantum Computing 12904:Applied computing 12890:Image compression 12660:Hardware security 12650:Security services 12607:Digital marketing 12394:Open-source model 12306:Modeling language 12218:Network scheduler 12039: 12038: 11801:Stephen Grossberg 11774: 11773: 10942:978-0-262-01243-0 10927:978-0-12-374856-0 10913:978-0-465-06570-7 10868:Robert Tibshirani 10847:Nils J. Nilsson, 10820:978-0-19-510270-3 10755:978-1-55860-467-4 10595:978-1-5386-9562-3 10507:978-3-9819263-4-7 10382:978-1-4673-9944-9 10214:. December 2019. 10043:Freedom to Tinker 9090:(6334): 183–186. 8795:www.kdnuggets.com 8511:978-0-12-824073-1 8216:978-3-9819263-4-7 8157:(11): 2479–2505. 7910:10.3390/s23187774 7691:also has similar 7585:978-0-13-461099-3 7560:978-1-9995795-0-0 7488:Shapiro, Ehud Y. 7282:978-3-030-58146-6 6927:978-1-60198-294-0 6670:978-0-444-86488-8 6550:978-3-642-27644-6 6336:978-1-58488-360-9 6276:978-0-262-01243-0 6242:. The MIT Press. 6140:978-3-319-18304-6 6125:. pp. 3–11. 6078:(6245): 255–260. 6049:978-0-262-01243-0 5946:Physical Review E 5774:Michael I. Jordan 5158:978-0-07-042807-2 5096:, 18 August 1961. 4791:978-0-387-31073-2 4626:978-94-010-6610-5 4332:IBM Watson Studio 4226:pandas (software) 3978:ethical questions 3913:Model assessments 3685:Telecommunication 3615:Medical diagnosis 3526:Credit-card fraud 3516:Computer networks 3378:decision boundary 3318:natural selection 3304:Genetic algorithm 3235:protein sequences 3191:Bayesian networks 3181:kernel regression 3153:linear regression 3119:linear classifier 3031:medical diagnosis 2955:artificial neuron 2846:logic programming 2820:genetic algorithm 2791:. In addition to 2677:Tomasz Imieliński 2636:Association rules 2593:Anomaly detection 2587:Anomaly detection 2518:Manifold learning 2494:, and supervised 2392:manifold learning 2250:estimated density 2238:similarity metric 2086:driving a vehicle 1944:Michael I. Jordan 1833:signal processing 1817:image compression 1763:arithmetic coding 1600:, as measured by 1391:without explicit 1358: 1357: 1094:Bayesian networks 1021:Intelligent agent 978: 977: 783:Model diagnostics 766:Human-in-the-loop 609:Boltzmann machine 522:Anomaly detection 318:Linear regression 233:Ontology learning 228:Grammar induction 203:Semantic analysis 198:Association rules 183:Anomaly detection 125:Neuro-symbolic AI 16:(Redirected from 13082: 13065:Machine learning 13042: 13041: 13032: 13031: 13022: 13021: 12842:Cross-validation 12814:Machine learning 12698:Social computing 12665:Network security 12460:Algorithm design 12389:Programming team 12349:Control variable 12326:Software library 12264:Software quality 12259:Operating system 12208:Network protocol 12073:Computer science 12066: 12059: 12052: 12043: 12042: 12029:Machine learning 12019: 12018: 11999: 11754:Action selection 11744:Self-driving car 11551:Stable Diffusion 11516:Speech synthesis 11481: 11480: 11345:Machine learning 11221:Gradient descent 11142: 11135: 11128: 11119: 11118: 11101:Machine learning 11098: 10836: 10834: 10832: 10798: 10771: 10769: 10767: 10747: 10733: 10699: 10698: 10688: 10678: 10654: 10648: 10647: 10645: 10644: 10625: 10619: 10618: 10616: 10615: 10569: 10563: 10562: 10560: 10559: 10537: 10531: 10530: 10528: 10527: 10491: 10471: 10465: 10464: 10462: 10461: 10450:syncedreview.com 10441: 10435: 10434: 10432: 10431: 10412: 10406: 10405: 10403: 10402: 10346: 10340: 10339: 10337: 10336: 10321: 10315: 10314: 10312: 10310: 10295: 10289: 10288: 10286: 10284: 10269:"AI and Compute" 10265: 10259: 10258: 10256: 10254: 10234: 10228: 10227: 10225: 10223: 10204: 10198: 10197: 10195: 10193: 10173: 10167: 10166: 10156: 10122: 10116: 10115: 10105: 10065: 10059: 10058: 10056: 10054: 10034: 10028: 10027: 10025: 10013: 10007: 10006: 10004: 10003: 9997: 9986: 9977: 9971: 9970: 9968: 9967: 9961: 9950: 9941: 9932: 9931: 9929: 9928: 9884: 9878: 9877: 9875: 9873: 9853: 9847: 9846: 9844: 9842: 9822: 9816: 9815: 9813: 9811: 9805: 9799:. Archived from 9798: 9789: 9783: 9782: 9746: 9740: 9739: 9737: 9736: 9730: 9719: 9710: 9704: 9703: 9701: 9689: 9683: 9682: 9680: 9678: 9657: 9651: 9650: 9648: 9646: 9625: 9619: 9618: 9616: 9615: 9596: 9590: 9589: 9587: 9575: 9569: 9568: 9566: 9564: 9545: 9539: 9538: 9536: 9534: 9515: 9509: 9503: 9497: 9491: 9485: 9484: 9474: 9450: 9444: 9443: 9433: 9401: 9395: 9394: 9392: 9391: 9363: 9357: 9356: 9354: 9353: 9333: 9327: 9326: 9324: 9323: 9309: 9301: 9295: 9294: 9292: 9291: 9275: 9267: 9261: 9260: 9258: 9257: 9237: 9231: 9230: 9228: 9197: 9188: 9177: 9176: 9175: 9174: 9168: 9157: 9148: 9142: 9141: 9099: 9079: 9073: 9072: 9036: 9027: 9026: 9016: 8998: 8974: 8968: 8967: 8965: 8963: 8943: 8937: 8936: 8934: 8933: 8903: 8897: 8896: 8894: 8893: 8874: 8868: 8867: 8865: 8864: 8845: 8839: 8838: 8836: 8835: 8819: 8810: 8809: 8807: 8806: 8787: 8781: 8780: 8778: 8777: 8758: 8752: 8751: 8749: 8748: 8729: 8723: 8722: 8720: 8719: 8700: 8694: 8693: 8691: 8690: 8650: 8644: 8643: 8641: 8640: 8622: 8582: 8576: 8575: 8573: 8572: 8532: 8526: 8525: 8524: 8523: 8493: 8487: 8486: 8476: 8458: 8434: 8428: 8427: 8425: 8424: 8406: 8381:(7): 2410–2424. 8375:Chemical Science 8366: 8360: 8359: 8357: 8356: 8308: 8302: 8301: 8299: 8298: 8276: 8270: 8269: 8267: 8266: 8246: 8240: 8239: 8237: 8236: 8200: 8185: 8179: 8178: 8176: 8166: 8142: 8136: 8135: 8125: 8115: 8091: 8085: 8084: 8082: 8081: 8061: 8055: 8035: 8029: 8028: 8026: 8024: 8008: 8002: 8001: 7999: 7997: 7977: 7971: 7970: 7968: 7966: 7951: 7945: 7944:research.att.com 7939: 7933: 7932: 7922: 7912: 7880: 7874: 7856: 7850: 7849: 7847: 7846: 7837:. 6 April 2017. 7827: 7821: 7820: 7792: 7786: 7785: 7769: 7763: 7762: 7760: 7759: 7753: 7738: 7722:Machine Learning 7718: 7709: 7703: 7685: 7679: 7678: 7676: 7674: 7654: 7648: 7647: 7645: 7629:Machine Learning 7616: 7610: 7596: 7590: 7589: 7571: 7565: 7564: 7546: 7540: 7526: 7520: 7506: 7500: 7486: 7480: 7466: 7460: 7459: 7449: 7425: 7419: 7418: 7390: 7374: 7368: 7367: 7357: 7332:(9): 3101–3116. 7317: 7311: 7300: 7294: 7293: 7274: 7257: 7251: 7250: 7240: 7230: 7220: 7196: 7190: 7189: 7159: 7153: 7152: 7150: 7149: 7143: 7132: 7123: 7117: 7116: 7114: 7113: 7107: 7084: 7066: 7057: 7051: 7050: 7025: 7019: 7002: 6996: 6995: 6969: 6949: 6943: 6942: 6940: 6939: 6908: 6902: 6901: 6899: 6898: 6892: 6870:(7): 1540–1551. 6861: 6852: 6846: 6845: 6837: 6831: 6830: 6828: 6827: 6821: 6814: 6803: 6797: 6796: 6794: 6793: 6787: 6780: 6769: 6763: 6762: 6750: 6744: 6743: 6717: 6708:(8): 1798–1828. 6697: 6691: 6688: 6682: 6679: 6673: 6661: 6655: 6654: 6628: 6622: 6621: 6619: 6617: 6561: 6555: 6554: 6528: 6522: 6521: 6519: 6518: 6498: 6492: 6491: 6481: 6471: 6447: 6441: 6440: 6430: 6408: 6402: 6401: 6388: 6382: 6381: 6371: 6347: 6341: 6340: 6322: 6316: 6315: 6313: 6312: 6298: 6292: 6291: 6289: 6288: 6260: 6254: 6253: 6235: 6229: 6228: 6208: 6202: 6201: 6191: 6159: 6153: 6152: 6118: 6112: 6111: 6067: 6061: 6060: 6058: 6056: 6041: 6031: 6025: 6024: 6003: 5997: 5996: 5962: 5940: 5934: 5933: 5923: 5913: 5889: 5883: 5882: 5880: 5879: 5861: 5855: 5854: 5852: 5850: 5832: 5808: 5802: 5799: 5793: 5792: 5790: 5789: 5770: 5761: 5760: 5750: 5712: 5706: 5705: 5703: 5702: 5672: 5666: 5665: 5663: 5662: 5647: 5641: 5640: 5638: 5637: 5622: 5616: 5615: 5613: 5612: 5597: 5591: 5590: 5588: 5576: 5570: 5569: 5567: 5565: 5550: 5544: 5543: 5510:Carla E. Brodley 5505: 5499: 5498: 5472: 5463: 5457: 5456: 5454: 5431: 5413: 5404: 5398: 5397: 5395: 5393: 5381: 5375: 5374: 5372: 5356:Machine Learning 5346: 5337: 5336: 5309: 5296: 5295: 5269: 5263: 5262: 5260: 5240: 5234: 5233: 5231: 5230: 5211: 5205: 5204: 5203: 5202: 5193:, archived from 5169: 5163: 5162: 5149:Machine Learning 5144: 5135: 5121: 5115: 5112: 5106: 5103: 5097: 5090: 5084: 5083: 5081: 5080: 5016: 5010: 5009: 5007: 5006: 4987: 4976: 4975: 4973: 4971: 4923: 4917: 4916: 4914: 4912: 4903:. Archived from 4892: 4886: 4883: 4874: 4873: 4855: 4835: 4829: 4828: 4813: 4804: 4801: 4795: 4794: 4774: 4763: 4762: 4752: 4742: 4727:Front. Plant Sci 4718: 4709: 4708: 4690: 4666: 4660: 4659: 4657: 4656: 4637: 4631: 4630: 4595: 4436:Machine Learning 4347:IBM SPSS Modeler 4233:(TMVA with ROOT) 4097:microcontrollers 4085:embedded systems 3982:algorithmic bias 3924:cross-validation 3907:white-box access 3802:Algorithmic bias 3771:black box theory 3730:Sun Microsystems 3715:held the first " 3645:Robot locomotion 3548:Financial market 3461:Adaptive website 3408:Algorithmic bias 3374:ensemble methods 3341:Belief functions 3328:to generate new 3310:search algorithm 3215:random variables 3197:Bayesian network 3165:ridge regression 3071:predictive model 2907: 2906: 2797:Web usage mining 2781: 2779: 2778: 2773: 2768: 2739: 2581:image de-noising 2565:strongly NP-hard 2563:. The method is 2470:Feature learning 2464:Feature learning 2192:Cluster analysis 2076:feature learning 1873:of (previously) 1744:Data compression 1702:computer science 1556:Raytheon Company 1545:Warren McCulloch 1476:machine learning 1361:Machine learning 1350: 1343: 1336: 1257:Existential risk 1079:Machine learning 980: 979: 970: 963: 956: 917:Related articles 794:Confusion matrix 547:Isolation forest 492:Graphical models 271: 270: 223:Learning to rank 218:Feature learning 56:Machine learning 47: 46: 33:Machine Learning 21: 18:Learning machine 13090: 13089: 13085: 13084: 13083: 13081: 13080: 13079: 13055: 13054: 13053: 13048: 13039: 13010: 12991:Word processing 12899: 12885:Virtual reality 12846: 12808: 12779:Computer vision 12755: 12751:Multiprocessing 12717: 12679: 12645:Security hacker 12621: 12597:Digital library 12538: 12489:Mathematics of 12484: 12446: 12422:Automata theory 12417:Formal language 12398: 12364:Software design 12335: 12268: 12254:Virtual machine 12232: 12228:Network service 12189: 12180:Embedded system 12153: 12086: 12075: 12070: 12040: 12035: 11987: 11901: 11867:Google DeepMind 11845: 11811:Geoffrey Hinton 11770: 11707: 11633:Project Debater 11579: 11477:Implementations 11472: 11426: 11390: 11333: 11275:Backpropagation 11209: 11195:Tensor calculus 11149: 11146: 11091: 11086: 11080:Wayback Machine 11060:Wayback Machine 11023:Wayback Machine 10974:Richard O. Duda 10960:Wayback Machine 10907:, Basic Books, 10884:Wayback Machine 10857:Wayback Machine 10843: 10841:Further reading 10830: 10828: 10821: 10805:Mackworth, Alan 10796: 10765: 10763: 10756: 10730: 10712:Domingos, Pedro 10707: 10702: 10655: 10651: 10642: 10640: 10627: 10626: 10622: 10613: 10611: 10596: 10570: 10566: 10557: 10555: 10538: 10534: 10525: 10523: 10508: 10472: 10468: 10459: 10457: 10442: 10438: 10429: 10427: 10414: 10413: 10409: 10400: 10398: 10383: 10347: 10343: 10334: 10332: 10323: 10322: 10318: 10308: 10306: 10301:. 27 May 2021. 10297: 10296: 10292: 10282: 10280: 10275:. 16 May 2018. 10267: 10266: 10262: 10252: 10250: 10235: 10231: 10221: 10219: 10212:InformationWeek 10206: 10205: 10201: 10191: 10189: 10174: 10170: 10139:(11): 981–983. 10123: 10119: 10080:(11): 981–983. 10066: 10062: 10052: 10050: 10035: 10031: 10014: 10010: 10001: 9999: 9995: 9984: 9978: 9974: 9965: 9963: 9959: 9948: 9942: 9935: 9926: 9924: 9885: 9881: 9871: 9869: 9854: 9850: 9840: 9838: 9823: 9819: 9809: 9807: 9806:on 4 March 2016 9803: 9796: 9790: 9786: 9747: 9743: 9734: 9732: 9728: 9717: 9711: 9707: 9690: 9686: 9676: 9674: 9669:. 10 May 2022. 9659: 9658: 9654: 9644: 9642: 9627: 9626: 9622: 9613: 9611: 9598: 9597: 9593: 9576: 9572: 9562: 9560: 9547: 9546: 9542: 9532: 9530: 9517: 9516: 9512: 9504: 9500: 9492: 9488: 9451: 9447: 9402: 9398: 9389: 9387: 9364: 9360: 9351: 9349: 9334: 9330: 9321: 9319: 9302: 9298: 9289: 9287: 9268: 9264: 9255: 9253: 9238: 9234: 9226: 9195: 9189: 9180: 9172: 9170: 9166: 9155: 9149: 9145: 9080: 9076: 9037: 9030: 8975: 8971: 8961: 8959: 8944: 8940: 8931: 8929: 8904: 8900: 8891: 8889: 8876: 8875: 8871: 8862: 8860: 8847: 8846: 8842: 8833: 8831: 8820: 8813: 8804: 8802: 8789: 8788: 8784: 8775: 8773: 8760: 8759: 8755: 8746: 8744: 8731: 8730: 8726: 8717: 8715: 8702: 8701: 8697: 8688: 8686: 8651: 8647: 8638: 8636: 8583: 8579: 8570: 8568: 8541:Fire Technology 8533: 8529: 8521: 8519: 8512: 8494: 8490: 8435: 8431: 8422: 8420: 8367: 8363: 8354: 8352: 8309: 8305: 8296: 8294: 8277: 8273: 8264: 8262: 8255:Business Weekly 8249:Quested, Tony. 8247: 8243: 8234: 8232: 8217: 8198: 8186: 8182: 8143: 8139: 8092: 8088: 8079: 8077: 8062: 8058: 8049:The Physics at 8045:Wayback Machine 8036: 8032: 8022: 8020: 8015:. Tech Crunch. 8009: 8005: 7995: 7993: 7978: 7974: 7964: 7962: 7953: 7952: 7948: 7940: 7936: 7881: 7877: 7871:Wayback Machine 7857: 7853: 7844: 7842: 7829: 7828: 7824: 7793: 7789: 7770: 7766: 7757: 7755: 7751: 7716: 7710: 7706: 7700:Wayback Machine 7686: 7682: 7672: 7670: 7655: 7651: 7620:Cortes, Corinna 7617: 7613: 7607:Wayback Machine 7597: 7593: 7586: 7572: 7568: 7561: 7547: 7543: 7537:Wayback Machine 7527: 7523: 7507: 7503: 7497:Wayback Machine 7487: 7483: 7477:Wayback Machine 7467: 7463: 7426: 7422: 7407: 7381:. p. 207. 7375: 7371: 7318: 7314: 7301: 7297: 7283: 7258: 7254: 7211:(1): e0226880. 7197: 7193: 7160: 7156: 7147: 7145: 7141: 7130: 7124: 7120: 7111: 7109: 7105: 7082:10.1.1.318.4023 7064: 7058: 7054: 7048: 7026: 7022: 7016:Wayback Machine 7003: 6999: 6950: 6946: 6937: 6935: 6928: 6909: 6905: 6896: 6894: 6890: 6859: 6853: 6849: 6838: 6834: 6825: 6823: 6819: 6812: 6804: 6800: 6791: 6789: 6785: 6778: 6770: 6766: 6751: 6747: 6698: 6694: 6689: 6685: 6680: 6676: 6662: 6658: 6651: 6629: 6625: 6615: 6613: 6562: 6558: 6551: 6529: 6525: 6516: 6514: 6499: 6495: 6448: 6444: 6409: 6405: 6389: 6385: 6356:Molecular Plant 6348: 6344: 6337: 6323: 6319: 6310: 6308: 6300: 6299: 6295: 6286: 6284: 6277: 6261: 6257: 6250: 6236: 6232: 6225: 6209: 6205: 6160: 6156: 6141: 6119: 6115: 6068: 6064: 6054: 6052: 6050: 6032: 6028: 6021: 6004: 6000: 5953:(3–1): 032118. 5941: 5937: 5890: 5886: 5877: 5875: 5862: 5858: 5848: 5846: 5809: 5805: 5800: 5796: 5787: 5785: 5771: 5764: 5715:Bzdok, Danilo; 5713: 5709: 5700: 5698: 5691: 5673: 5669: 5660: 5658: 5648: 5644: 5635: 5633: 5624: 5623: 5619: 5610: 5608: 5599: 5598: 5594: 5577: 5573: 5563: 5561: 5551: 5547: 5532: 5516:. p. 332. 5506: 5502: 5470: 5464: 5460: 5452: 5429:10.1.1.627.3751 5411: 5405: 5401: 5391: 5389: 5384:Mahoney, Matt. 5382: 5378: 5347: 5340: 5333: 5313:Russell, Stuart 5310: 5299: 5284: 5270: 5266: 5241: 5237: 5228: 5226: 5213: 5212: 5208: 5200: 5198: 5191: 5170: 5166: 5159: 5145: 5138: 5133:Wayback Machine 5122: 5118: 5113: 5109: 5104: 5100: 5094:Time (magazine) 5091: 5087: 5078: 5076: 5017: 5013: 5004: 5002: 4989: 4988: 4979: 4969: 4967: 4924: 4920: 4910: 4908: 4893: 4889: 4884: 4877: 4853:10.1.1.368.2254 4836: 4832: 4814: 4807: 4802: 4798: 4792: 4775: 4766: 4719: 4712: 4667: 4663: 4654: 4652: 4639: 4638: 4634: 4627: 4596: 4592: 4588: 4543: 4459: 4427: 4422: 4372:Neural Designer 4323:KnowledgeSTUDIO 4311: 4294: 4289: 4136: 4128:Software suites 4125: 4078: 4046: 4025: 3974: 3960: 3915: 3882: 3865: 3859: 3850: 3844: 3804: 3798: 3763: 3738:Springer Nature 3709: 3690:Theorem proving 3660:Sequence mining 3521:Computer vision 3511:Climate Science 3506:Citizen Science 3501:Cheminformatics 3443: 3422: 3416: 3386: 3384:Training models 3349: 3343: 3306: 3300: 3257: 3251: 3211:graphical model 3199: 3193: 3175:(often used in 3141: 3135: 3102: 3096: 3088:decision making 3052: 3046: 3011:computer vision 2939: 2933: 2927: 2919:model selection 2904: 2903: 2899: 2885:here refers to 2813:sequence mining 2749: 2693: 2688: 2685: 2684: 2650: 2644: 2638: 2623: 2595: 2589: 2557:basis functions 2553: 2547: 2472: 2466: 2434:perform action 2420: 2404: 2365: 2308:software agents 2304: 2298: 2275: 2269: 2194: 2188: 2182: 2151:active learning 2116:linear boundary 2104: 2098: 2054: 2046:time complexity 2042:polynomial time 2003: 1995:Main articles: 1993: 1977: 1928: 1916: 1891:reproduce known 1859: 1854: 1853: 1754: 1746: 1722:backpropagation 1669:of statistics. 1655:neural networks 1635: 1630: 1582:Tom M. Mitchell 1488:computer gaming 1472: 1466: 1416:email filtering 1408:computer vision 1354: 1325: 1324: 1315: 1307: 1306: 1282: 1272: 1271: 1243:Control problem 1223: 1213: 1212: 1124: 1114: 1113: 1074: 1066: 1065: 1036:Computer vision 1011: 974: 945: 944: 918: 910: 909: 870: 862: 861: 822:Kernel machines 817: 809: 808: 784: 776: 775: 756:Active learning 751: 743: 742: 711: 701: 700: 626:Diffusion model 562: 552: 551: 524: 514: 513: 487: 477: 476: 432:Factor analysis 427: 417: 416: 400: 363: 353: 352: 273: 272: 256: 255: 254: 243: 242: 148: 140: 139: 105:Online learning 70: 58: 45: 38: 28: 23: 22: 15: 12: 11: 5: 13088: 13078: 13077: 13072: 13067: 13050: 13049: 13047: 13046: 13036: 13026: 13015: 13012: 13011: 13009: 13008: 13003: 12998: 12993: 12988: 12983: 12978: 12973: 12968: 12963: 12958: 12953: 12948: 12943: 12938: 12933: 12928: 12923: 12918: 12913: 12907: 12905: 12901: 12900: 12898: 12897: 12895:Solid modeling 12892: 12887: 12882: 12877: 12872: 12867: 12862: 12856: 12854: 12848: 12847: 12845: 12844: 12839: 12834: 12829: 12824: 12818: 12816: 12810: 12809: 12807: 12806: 12801: 12796: 12794:Control method 12791: 12786: 12781: 12776: 12771: 12765: 12763: 12757: 12756: 12754: 12753: 12748: 12746:Multithreading 12743: 12738: 12733: 12727: 12725: 12719: 12718: 12716: 12715: 12710: 12705: 12700: 12695: 12689: 12687: 12681: 12680: 12678: 12677: 12672: 12667: 12662: 12657: 12652: 12647: 12642: 12640:Formal methods 12637: 12631: 12629: 12623: 12622: 12620: 12619: 12614: 12612:World Wide Web 12609: 12604: 12599: 12594: 12589: 12584: 12579: 12574: 12569: 12564: 12559: 12554: 12548: 12546: 12540: 12539: 12537: 12536: 12531: 12526: 12521: 12516: 12511: 12506: 12501: 12495: 12493: 12486: 12485: 12483: 12482: 12477: 12472: 12467: 12462: 12456: 12454: 12448: 12447: 12445: 12444: 12439: 12434: 12429: 12424: 12419: 12414: 12408: 12406: 12400: 12399: 12397: 12396: 12391: 12386: 12381: 12376: 12371: 12366: 12361: 12356: 12351: 12345: 12343: 12337: 12336: 12334: 12333: 12328: 12323: 12318: 12313: 12308: 12303: 12298: 12293: 12288: 12282: 12280: 12270: 12269: 12267: 12266: 12261: 12256: 12251: 12246: 12240: 12238: 12234: 12233: 12231: 12230: 12225: 12220: 12215: 12210: 12205: 12199: 12197: 12191: 12190: 12188: 12187: 12182: 12177: 12172: 12167: 12161: 12159: 12155: 12154: 12152: 12151: 12142: 12137: 12132: 12127: 12122: 12117: 12112: 12107: 12102: 12096: 12094: 12088: 12087: 12080: 12077: 12076: 12069: 12068: 12061: 12054: 12046: 12037: 12036: 12034: 12033: 12032: 12031: 12026: 12013: 12012: 12011: 12006: 11992: 11989: 11988: 11986: 11985: 11980: 11975: 11970: 11965: 11960: 11955: 11950: 11945: 11940: 11935: 11930: 11925: 11920: 11915: 11909: 11907: 11903: 11902: 11900: 11899: 11894: 11889: 11884: 11879: 11874: 11869: 11864: 11859: 11853: 11851: 11847: 11846: 11844: 11843: 11841:Ilya Sutskever 11838: 11833: 11828: 11823: 11818: 11813: 11808: 11806:Demis Hassabis 11803: 11798: 11796:Ian Goodfellow 11793: 11788: 11782: 11780: 11776: 11775: 11772: 11771: 11769: 11768: 11763: 11762: 11761: 11751: 11746: 11741: 11736: 11731: 11726: 11721: 11715: 11713: 11709: 11708: 11706: 11705: 11700: 11695: 11690: 11685: 11680: 11675: 11670: 11665: 11660: 11655: 11650: 11645: 11640: 11635: 11630: 11625: 11624: 11623: 11613: 11608: 11603: 11598: 11593: 11587: 11585: 11581: 11580: 11578: 11577: 11572: 11571: 11570: 11565: 11555: 11554: 11553: 11548: 11543: 11533: 11528: 11523: 11518: 11513: 11508: 11503: 11498: 11493: 11487: 11485: 11478: 11474: 11473: 11471: 11470: 11465: 11460: 11455: 11450: 11445: 11440: 11434: 11432: 11428: 11427: 11425: 11424: 11419: 11414: 11409: 11404: 11398: 11396: 11392: 11391: 11389: 11388: 11387: 11386: 11379:Language model 11376: 11371: 11366: 11365: 11364: 11354: 11353: 11352: 11341: 11339: 11335: 11334: 11332: 11331: 11329:Autoregression 11326: 11321: 11320: 11319: 11309: 11307:Regularization 11304: 11303: 11302: 11297: 11292: 11282: 11277: 11272: 11270:Loss functions 11267: 11262: 11257: 11252: 11247: 11246: 11245: 11235: 11230: 11229: 11228: 11217: 11215: 11211: 11210: 11208: 11207: 11205:Inductive bias 11202: 11197: 11192: 11187: 11182: 11177: 11172: 11167: 11159: 11157: 11151: 11150: 11145: 11144: 11137: 11130: 11122: 11116: 11115: 11109: 11104: 11090: 11089:External links 11087: 11085: 11084: 11068: 11048:Ray Solomonoff 11045: 11038:Ray Solomonoff 11035: 11011: 10993: 10971: 10945: 10930: 10915: 10899:Pedro Domingos 10896: 10861: 10844: 10842: 10839: 10838: 10837: 10819: 10803:Poole, David; 10800: 10794: 10772: 10754: 10734: 10729:978-0465065707 10728: 10706: 10703: 10701: 10700: 10649: 10620: 10594: 10564: 10532: 10506: 10466: 10436: 10422:. 2021-06-02. 10407: 10381: 10341: 10316: 10290: 10260: 10229: 10199: 10182:airesearch.com 10168: 10117: 10060: 10029: 10008: 9972: 9933: 9879: 9848: 9817: 9784: 9757:(3): 570–583. 9741: 9705: 9684: 9652: 9620: 9591: 9570: 9540: 9510: 9508:, p. 286. 9498: 9486: 9445: 9416:(5): 206–215. 9396: 9358: 9328: 9296: 9279:New York Times 9262: 9232: 9178: 9143: 9074: 9047:(4): 111–117. 9028: 8969: 8938: 8898: 8884:. 2018-07-25. 8869: 8840: 8811: 8782: 8768:. 2016-09-18. 8753: 8739:. 2017-04-18. 8724: 8695: 8645: 8577: 8547:(2): 793–825. 8527: 8510: 8488: 8449:(1): 134–148. 8429: 8361: 8303: 8271: 8241: 8215: 8180: 8137: 8106:(4): 337–339. 8086: 8056: 8030: 8003: 7972: 7961:on 31 May 2016 7946: 7934: 7875: 7860:CFA Curriculum 7851: 7835:Google AI Blog 7822: 7787: 7764: 7704: 7680: 7649: 7636:(3): 273–297. 7611: 7591: 7584: 7566: 7559: 7541: 7521: 7501: 7481: 7461: 7420: 7406:978-0897915922 7405: 7388:10.1.1.40.6984 7369: 7326:The Plant Cell 7312: 7295: 7281: 7252: 7191: 7154: 7118: 7052: 7046: 7020: 6997: 6944: 6926: 6903: 6847: 6832: 6798: 6764: 6745: 6692: 6683: 6674: 6656: 6650:978-3540732624 6649: 6623: 6556: 6549: 6523: 6493: 6442: 6403: 6383: 6362:(6): 975–978. 6342: 6335: 6317: 6293: 6275: 6255: 6248: 6230: 6223: 6203: 6154: 6139: 6113: 6062: 6048: 6026: 6019: 6007:Mohri, Mehryar 5998: 5935: 5884: 5856: 5803: 5794: 5776:(2014-09-10). 5762: 5733:(4): 233–234. 5726:Nature Methods 5707: 5689: 5667: 5642: 5617: 5592: 5571: 5545: 5530: 5500: 5481:(3): 381–388. 5458: 5422:(2): 131–154. 5399: 5376: 5338: 5332:978-0137903955 5331: 5297: 5282: 5264: 5235: 5221:. 2020-12-08. 5206: 5189: 5173:Harnad, Stevan 5164: 5157: 5136: 5116: 5107: 5098: 5085: 5031:(1): 124–129. 5011: 4977: 4918: 4887: 4875: 4846:(3): 210–229. 4830: 4805: 4796: 4790: 4764: 4710: 4661: 4632: 4625: 4589: 4587: 4584: 4583: 4582: 4577: 4572: 4567: 4562: 4556: 4550: 4542: 4539: 4538: 4537: 4528: 4519: 4510: 4501: 4492: 4483: 4474: 4465: 4458: 4455: 4454: 4453: 4448: 4443: 4438: 4433: 4426: 4423: 4421: 4420: 4414: 4409: 4404: 4399: 4394: 4389: 4384: 4379: 4377:NeuroSolutions 4374: 4369: 4364: 4359: 4354: 4349: 4344: 4339: 4334: 4329: 4324: 4318: 4312: 4310: 4307: 4306: 4305: 4300: 4293: 4290: 4288: 4287: 4282: 4277: 4268: 4259: 4254: 4249: 4244: 4239: 4234: 4228: 4223: 4218: 4213: 4208: 4203: 4198: 4193: 4188: 4183: 4178: 4173: 4168: 4163: 4158: 4153: 4148: 4146:Deeplearning4j 4143: 4137: 4135: 4132: 4124: 4121: 4077: 4074: 4062:neural synapse 4045: 4042: 4024: 4021: 3959: 3956: 3914: 3911: 3881: 3878: 3861:Main article: 3858: 3855: 3846:Main article: 3843: 3842:Explainability 3840: 3800:Main article: 3797: 3794: 3762: 3759: 3725:ensemble model 3708: 3707: 3702: 3697: 3692: 3687: 3682: 3677: 3672: 3667: 3662: 3657: 3652: 3650:Search engines 3647: 3642: 3637: 3632: 3627: 3622: 3617: 3612: 3607: 3602: 3597: 3592: 3587: 3582: 3579:Internet fraud 3576: 3571: 3566: 3561: 3556: 3551: 3545: 3540: 3539:classification 3534: 3529: 3523: 3518: 3513: 3508: 3503: 3498: 3493: 3491:Bioinformatics 3488: 3483: 3478: 3473: 3468: 3463: 3458: 3453: 3447: 3442: 3439: 3418:Main article: 3415: 3412: 3395:corpus of text 3385: 3382: 3345:Main article: 3342: 3339: 3302:Main article: 3299: 3296: 3253:Main article: 3250: 3247: 3231:speech signals 3195:Main article: 3192: 3189: 3161:regularization 3137:Main article: 3134: 3131: 3098:Main article: 3095: 3092: 3048:Main article: 3045: 3044:Decision trees 3042: 3023:social network 2929:Main article: 2926: 2923: 2898: 2895: 2874:Gordon Plotkin 2866:bioinformatics 2809:bioinformatics 2771: 2767: 2764: 2761: 2758: 2755: 2752: 2748: 2745: 2742: 2738: 2735: 2732: 2729: 2726: 2723: 2720: 2717: 2714: 2711: 2708: 2705: 2702: 2699: 2696: 2692: 2673:Rakesh Agrawal 2640:Main article: 2637: 2634: 2626:Robot learning 2622: 2621:Robot learning 2619: 2591:Main article: 2588: 2585: 2549:Main article: 2546: 2543: 2468:Main article: 2465: 2462: 2457: 2456: 2450: 2444: 2438: 2419: 2416: 2408:topic modeling 2403: 2400: 2364: 2361: 2320:control theory 2310:ought to take 2300:Main article: 2297: 2294: 2271:Main article: 2268: 2265: 2184:Main article: 2181: 2178: 2155:classification 2132:feature vector 2100:Main article: 2097: 2094: 2090: 2089: 2079: 2069: 2053: 2050: 1992: 1989: 1976: 1973: 1927: 1924: 1915: 1914:Generalization 1912: 1858: 1855: 1755: 1747: 1745: 1742: 1686:expert systems 1634: 1631: 1629: 1626: 1494:. The synonym 1465: 1462: 1449:(EDA) through 1369:field of study 1356: 1355: 1353: 1352: 1345: 1338: 1330: 1327: 1326: 1323: 1322: 1316: 1313: 1312: 1309: 1308: 1305: 1304: 1299: 1294: 1289: 1283: 1278: 1277: 1274: 1273: 1270: 1269: 1264: 1259: 1254: 1249: 1240: 1235: 1230: 1224: 1219: 1218: 1215: 1214: 1211: 1210: 1205: 1200: 1195: 1190: 1189: 1188: 1178: 1173: 1168: 1167: 1166: 1161: 1156: 1146: 1141: 1139:Earth sciences 1136: 1131: 1129:Bioinformatics 1125: 1120: 1119: 1116: 1115: 1112: 1111: 1106: 1101: 1096: 1091: 1086: 1081: 1075: 1072: 1071: 1068: 1067: 1064: 1063: 1058: 1053: 1048: 1043: 1038: 1033: 1028: 1023: 1018: 1012: 1007: 1006: 1003: 1002: 992: 991: 985: 984: 976: 975: 973: 972: 965: 958: 950: 947: 946: 943: 942: 937: 936: 935: 925: 919: 916: 915: 912: 911: 908: 907: 902: 897: 892: 887: 882: 877: 871: 868: 867: 864: 863: 860: 859: 854: 849: 844: 842:Occam learning 839: 834: 829: 824: 818: 815: 814: 811: 810: 807: 806: 801: 799:Learning curve 796: 791: 785: 782: 781: 778: 777: 774: 773: 768: 763: 758: 752: 749: 748: 745: 744: 741: 740: 739: 738: 728: 723: 718: 712: 707: 706: 703: 702: 699: 698: 692: 687: 682: 677: 676: 675: 665: 660: 659: 658: 653: 648: 643: 633: 628: 623: 618: 617: 616: 606: 605: 604: 599: 594: 589: 579: 574: 569: 563: 558: 557: 554: 553: 550: 549: 544: 539: 531: 525: 520: 519: 516: 515: 512: 511: 510: 509: 504: 499: 488: 483: 482: 479: 478: 475: 474: 469: 464: 459: 454: 449: 444: 439: 434: 428: 423: 422: 419: 418: 415: 414: 409: 404: 398: 393: 388: 380: 375: 370: 364: 359: 358: 355: 354: 351: 350: 345: 340: 335: 330: 325: 320: 315: 307: 306: 305: 300: 295: 285: 283:Decision trees 280: 274: 260:classification 250: 249: 248: 245: 244: 241: 240: 235: 230: 225: 220: 215: 210: 205: 200: 195: 190: 185: 180: 175: 170: 165: 160: 155: 153:Classification 149: 146: 145: 142: 141: 138: 137: 132: 127: 122: 117: 112: 110:Batch learning 107: 102: 97: 92: 87: 82: 77: 71: 68: 67: 64: 63: 52: 51: 26: 9: 6: 4: 3: 2: 13087: 13076: 13073: 13071: 13068: 13066: 13063: 13062: 13060: 13045: 13037: 13035: 13027: 13025: 13017: 13016: 13013: 13007: 13004: 13002: 12999: 12997: 12994: 12992: 12989: 12987: 12984: 12982: 12979: 12977: 12974: 12972: 12969: 12967: 12964: 12962: 12959: 12957: 12954: 12952: 12949: 12947: 12944: 12942: 12939: 12937: 12934: 12932: 12929: 12927: 12924: 12922: 12919: 12917: 12914: 12912: 12909: 12908: 12906: 12902: 12896: 12893: 12891: 12888: 12886: 12883: 12881: 12880:Mixed reality 12878: 12876: 12873: 12871: 12868: 12866: 12863: 12861: 12858: 12857: 12855: 12853: 12849: 12843: 12840: 12838: 12835: 12833: 12830: 12828: 12825: 12823: 12820: 12819: 12817: 12815: 12811: 12805: 12802: 12800: 12797: 12795: 12792: 12790: 12787: 12785: 12782: 12780: 12777: 12775: 12772: 12770: 12767: 12766: 12764: 12762: 12758: 12752: 12749: 12747: 12744: 12742: 12739: 12737: 12734: 12732: 12729: 12728: 12726: 12724: 12720: 12714: 12713:Accessibility 12711: 12709: 12708:Visualization 12706: 12704: 12701: 12699: 12696: 12694: 12691: 12690: 12688: 12686: 12682: 12676: 12673: 12671: 12668: 12666: 12663: 12661: 12658: 12656: 12653: 12651: 12648: 12646: 12643: 12641: 12638: 12636: 12633: 12632: 12630: 12628: 12624: 12618: 12615: 12613: 12610: 12608: 12605: 12603: 12600: 12598: 12595: 12593: 12590: 12588: 12585: 12583: 12580: 12578: 12575: 12573: 12570: 12568: 12565: 12563: 12560: 12558: 12555: 12553: 12550: 12549: 12547: 12545: 12541: 12535: 12532: 12530: 12527: 12525: 12522: 12520: 12517: 12515: 12512: 12510: 12507: 12505: 12502: 12500: 12497: 12496: 12494: 12492: 12487: 12481: 12478: 12476: 12473: 12471: 12468: 12466: 12463: 12461: 12458: 12457: 12455: 12453: 12449: 12443: 12440: 12438: 12435: 12433: 12430: 12428: 12425: 12423: 12420: 12418: 12415: 12413: 12410: 12409: 12407: 12405: 12401: 12395: 12392: 12390: 12387: 12385: 12382: 12380: 12377: 12375: 12372: 12370: 12367: 12365: 12362: 12360: 12357: 12355: 12352: 12350: 12347: 12346: 12344: 12342: 12338: 12332: 12329: 12327: 12324: 12322: 12319: 12317: 12314: 12312: 12309: 12307: 12304: 12302: 12299: 12297: 12294: 12292: 12289: 12287: 12284: 12283: 12281: 12279: 12275: 12271: 12265: 12262: 12260: 12257: 12255: 12252: 12250: 12247: 12245: 12242: 12241: 12239: 12235: 12229: 12226: 12224: 12221: 12219: 12216: 12214: 12211: 12209: 12206: 12204: 12201: 12200: 12198: 12196: 12192: 12186: 12183: 12181: 12178: 12176: 12175:Dependability 12173: 12171: 12168: 12166: 12163: 12162: 12160: 12156: 12150: 12146: 12143: 12141: 12138: 12136: 12133: 12131: 12128: 12126: 12123: 12121: 12118: 12116: 12113: 12111: 12108: 12106: 12103: 12101: 12098: 12097: 12095: 12093: 12089: 12084: 12078: 12074: 12067: 12062: 12060: 12055: 12053: 12048: 12047: 12044: 12030: 12027: 12025: 12022: 12021: 12014: 12010: 12007: 12005: 12002: 12001: 11998: 11994: 11993: 11990: 11984: 11981: 11979: 11976: 11974: 11971: 11969: 11966: 11964: 11961: 11959: 11956: 11954: 11951: 11949: 11946: 11944: 11941: 11939: 11936: 11934: 11931: 11929: 11926: 11924: 11921: 11919: 11916: 11914: 11911: 11910: 11908: 11906:Architectures 11904: 11898: 11895: 11893: 11890: 11888: 11885: 11883: 11880: 11878: 11875: 11873: 11870: 11868: 11865: 11863: 11860: 11858: 11855: 11854: 11852: 11850:Organizations 11848: 11842: 11839: 11837: 11834: 11832: 11829: 11827: 11824: 11822: 11819: 11817: 11814: 11812: 11809: 11807: 11804: 11802: 11799: 11797: 11794: 11792: 11789: 11787: 11786:Yoshua Bengio 11784: 11783: 11781: 11777: 11767: 11766:Robot control 11764: 11760: 11757: 11756: 11755: 11752: 11750: 11747: 11745: 11742: 11740: 11737: 11735: 11732: 11730: 11727: 11725: 11722: 11720: 11717: 11716: 11714: 11710: 11704: 11701: 11699: 11696: 11694: 11691: 11689: 11686: 11684: 11683:Chinchilla AI 11681: 11679: 11676: 11674: 11671: 11669: 11666: 11664: 11661: 11659: 11656: 11654: 11651: 11649: 11646: 11644: 11641: 11639: 11636: 11634: 11631: 11629: 11626: 11622: 11619: 11618: 11617: 11614: 11612: 11609: 11607: 11604: 11602: 11599: 11597: 11594: 11592: 11589: 11588: 11586: 11582: 11576: 11573: 11569: 11566: 11564: 11561: 11560: 11559: 11556: 11552: 11549: 11547: 11544: 11542: 11539: 11538: 11537: 11534: 11532: 11529: 11527: 11524: 11522: 11519: 11517: 11514: 11512: 11509: 11507: 11504: 11502: 11499: 11497: 11494: 11492: 11489: 11488: 11486: 11482: 11479: 11475: 11469: 11466: 11464: 11461: 11459: 11456: 11454: 11451: 11449: 11446: 11444: 11441: 11439: 11436: 11435: 11433: 11429: 11423: 11420: 11418: 11415: 11413: 11410: 11408: 11405: 11403: 11400: 11399: 11397: 11393: 11385: 11382: 11381: 11380: 11377: 11375: 11372: 11370: 11367: 11363: 11362:Deep learning 11360: 11359: 11358: 11355: 11351: 11348: 11347: 11346: 11343: 11342: 11340: 11336: 11330: 11327: 11325: 11322: 11318: 11315: 11314: 11313: 11310: 11308: 11305: 11301: 11298: 11296: 11293: 11291: 11288: 11287: 11286: 11283: 11281: 11278: 11276: 11273: 11271: 11268: 11266: 11263: 11261: 11258: 11256: 11253: 11251: 11250:Hallucination 11248: 11244: 11241: 11240: 11239: 11236: 11234: 11231: 11227: 11224: 11223: 11222: 11219: 11218: 11216: 11212: 11206: 11203: 11201: 11198: 11196: 11193: 11191: 11188: 11186: 11183: 11181: 11178: 11176: 11173: 11171: 11168: 11166: 11165: 11161: 11160: 11158: 11156: 11152: 11143: 11138: 11136: 11131: 11129: 11124: 11123: 11120: 11113: 11110: 11108: 11105: 11102: 11097: 11093: 11092: 11082: 11081: 11077: 11074: 11069: 11066: 11062: 11061: 11057: 11054: 11049: 11046: 11043: 11039: 11036: 11033: 11032:9789332543515 11029: 11025: 11024: 11020: 11017: 11012: 11009: 11008:0-19-853864-2 11005: 11001: 10997: 10994: 10991: 10990:0-471-05669-3 10987: 10983: 10979: 10978:Peter E. Hart 10975: 10972: 10970: 10969:0-521-64298-1 10966: 10962: 10961: 10957: 10954: 10949: 10946: 10943: 10939: 10936:, MIT Press, 10935: 10931: 10928: 10924: 10920: 10916: 10914: 10910: 10906: 10905: 10900: 10897: 10894: 10893:0-387-95284-5 10890: 10886: 10885: 10881: 10878: 10873: 10869: 10865: 10864:Trevor Hastie 10862: 10859: 10858: 10854: 10851: 10846: 10845: 10826: 10822: 10816: 10812: 10811: 10806: 10801: 10797: 10795:0-13-790395-2 10791: 10787: 10786: 10781: 10780:Norvig, Peter 10777: 10773: 10761: 10757: 10751: 10746: 10745: 10739: 10738:Nilsson, Nils 10735: 10731: 10725: 10721: 10717: 10713: 10709: 10708: 10696: 10692: 10687: 10682: 10677: 10672: 10668: 10664: 10660: 10653: 10638: 10634: 10630: 10624: 10609: 10605: 10601: 10597: 10591: 10587: 10583: 10579: 10575: 10568: 10553: 10549: 10548: 10543: 10536: 10521: 10517: 10513: 10509: 10503: 10499: 10495: 10490: 10485: 10481: 10477: 10470: 10455: 10451: 10447: 10440: 10425: 10421: 10417: 10411: 10396: 10392: 10388: 10384: 10378: 10374: 10370: 10366: 10362: 10358: 10357: 10352: 10345: 10330: 10326: 10320: 10304: 10300: 10294: 10278: 10274: 10270: 10264: 10248: 10244: 10240: 10233: 10217: 10213: 10209: 10203: 10187: 10183: 10179: 10172: 10164: 10160: 10155: 10150: 10146: 10142: 10138: 10134: 10133: 10128: 10121: 10113: 10109: 10104: 10099: 10095: 10091: 10087: 10083: 10079: 10075: 10071: 10064: 10048: 10044: 10040: 10033: 10024: 10019: 10012: 9994: 9990: 9983: 9976: 9958: 9954: 9947: 9940: 9938: 9922: 9918: 9914: 9910: 9906: 9902: 9898: 9894: 9890: 9883: 9867: 9863: 9859: 9852: 9836: 9832: 9828: 9821: 9802: 9795: 9788: 9780: 9776: 9772: 9768: 9764: 9760: 9756: 9752: 9745: 9727: 9723: 9716: 9709: 9700: 9695: 9688: 9672: 9668: 9667: 9666:IEEE Spectrum 9662: 9656: 9640: 9636: 9635: 9630: 9624: 9609: 9605: 9601: 9595: 9586: 9581: 9574: 9558: 9554: 9550: 9544: 9528: 9524: 9520: 9514: 9507: 9506:Domingos 2015 9502: 9495: 9494:Domingos 2015 9490: 9482: 9478: 9473: 9468: 9464: 9460: 9456: 9449: 9441: 9437: 9432: 9427: 9423: 9419: 9415: 9411: 9407: 9400: 9385: 9381: 9377: 9373: 9369: 9362: 9347: 9343: 9339: 9332: 9317: 9313: 9308: 9300: 9285: 9281: 9280: 9274: 9266: 9251: 9247: 9243: 9236: 9225: 9221: 9217: 9213: 9209: 9205: 9201: 9194: 9187: 9185: 9183: 9165: 9161: 9154: 9147: 9139: 9135: 9131: 9127: 9123: 9119: 9115: 9111: 9107: 9103: 9098: 9093: 9089: 9085: 9078: 9070: 9066: 9062: 9058: 9054: 9050: 9046: 9042: 9035: 9033: 9024: 9020: 9015: 9010: 9006: 9002: 8997: 8992: 8988: 8984: 8980: 8973: 8957: 8953: 8949: 8942: 8927: 8923: 8919: 8915: 8914: 8909: 8902: 8887: 8883: 8879: 8873: 8858: 8854: 8853:The Economist 8850: 8844: 8829: 8825: 8818: 8816: 8800: 8796: 8792: 8786: 8771: 8767: 8763: 8757: 8742: 8738: 8734: 8728: 8714:on 2017-03-20 8713: 8709: 8708:Bloomberg.com 8705: 8699: 8684: 8680: 8676: 8672: 8668: 8664: 8660: 8656: 8649: 8634: 8630: 8626: 8621: 8616: 8612: 8608: 8604: 8600: 8596: 8592: 8588: 8581: 8566: 8562: 8558: 8554: 8550: 8546: 8542: 8538: 8531: 8517: 8513: 8507: 8503: 8499: 8492: 8484: 8480: 8475: 8470: 8466: 8462: 8457: 8452: 8448: 8444: 8440: 8433: 8418: 8414: 8410: 8405: 8400: 8396: 8392: 8388: 8384: 8380: 8376: 8372: 8365: 8350: 8346: 8342: 8338: 8334: 8330: 8326: 8322: 8318: 8314: 8307: 8292: 8288: 8287: 8282: 8275: 8260: 8256: 8252: 8245: 8230: 8226: 8222: 8218: 8212: 8208: 8204: 8197: 8196: 8191: 8184: 8175: 8170: 8165: 8160: 8156: 8152: 8148: 8141: 8133: 8129: 8124: 8119: 8114: 8109: 8105: 8101: 8097: 8090: 8075: 8071: 8067: 8060: 8054: 8052: 8046: 8042: 8039: 8034: 8018: 8014: 8007: 7991: 7987: 7983: 7976: 7960: 7956: 7950: 7943: 7938: 7930: 7926: 7921: 7916: 7911: 7906: 7902: 7898: 7894: 7890: 7886: 7879: 7872: 7868: 7865: 7861: 7855: 7840: 7836: 7832: 7826: 7818: 7814: 7810: 7806: 7802: 7798: 7791: 7783: 7779: 7775: 7768: 7750: 7746: 7742: 7737: 7732: 7728: 7724: 7723: 7715: 7708: 7701: 7697: 7694: 7690: 7684: 7668: 7664: 7660: 7653: 7644: 7639: 7635: 7631: 7630: 7625: 7621: 7615: 7608: 7604: 7601: 7595: 7587: 7581: 7577: 7570: 7562: 7556: 7552: 7545: 7538: 7534: 7531: 7525: 7519: 7518:0-262-19218-7 7515: 7511: 7505: 7498: 7494: 7491: 7485: 7478: 7474: 7471: 7468:Plotkin G.D. 7465: 7457: 7453: 7448: 7443: 7439: 7435: 7431: 7424: 7416: 7412: 7408: 7402: 7398: 7394: 7389: 7384: 7380: 7373: 7365: 7361: 7356: 7351: 7347: 7343: 7339: 7335: 7331: 7327: 7323: 7316: 7309: 7305: 7299: 7292: 7288: 7284: 7278: 7273: 7268: 7264: 7256: 7248: 7244: 7239: 7234: 7229: 7224: 7219: 7214: 7210: 7206: 7202: 7195: 7187: 7183: 7179: 7175: 7171: 7167: 7166: 7158: 7140: 7136: 7129: 7122: 7104: 7100: 7096: 7092: 7088: 7083: 7078: 7075:(2): 85–126. 7074: 7070: 7063: 7056: 7049: 7047:9781489979933 7043: 7039: 7035: 7031: 7024: 7017: 7013: 7010: 7006: 7001: 6993: 6989: 6985: 6981: 6977: 6973: 6968: 6963: 6959: 6955: 6948: 6933: 6929: 6923: 6919: 6918: 6913: 6912:Yoshua Bengio 6907: 6889: 6885: 6881: 6877: 6873: 6869: 6865: 6858: 6851: 6843: 6836: 6818: 6811: 6810: 6802: 6788:on 2017-08-13 6784: 6777: 6776: 6768: 6760: 6756: 6749: 6741: 6737: 6733: 6729: 6725: 6721: 6716: 6711: 6707: 6703: 6696: 6687: 6678: 6671: 6667: 6660: 6652: 6646: 6642: 6638: 6634: 6627: 6611: 6607: 6603: 6599: 6595: 6591: 6587: 6583: 6579: 6575: 6571: 6567: 6560: 6552: 6546: 6542: 6538: 6534: 6527: 6513:on 2019-06-06 6512: 6508: 6504: 6497: 6489: 6485: 6480: 6475: 6470: 6465: 6461: 6457: 6453: 6446: 6438: 6434: 6429: 6424: 6420: 6416: 6415: 6407: 6399: 6395: 6387: 6379: 6375: 6370: 6365: 6361: 6357: 6353: 6346: 6338: 6332: 6328: 6321: 6307: 6303: 6297: 6282: 6278: 6272: 6268: 6267: 6259: 6251: 6249:9780262018258 6245: 6241: 6234: 6226: 6224:9780136042594 6220: 6216: 6215: 6207: 6199: 6195: 6190: 6185: 6181: 6177: 6173: 6169: 6165: 6158: 6150: 6146: 6142: 6136: 6132: 6128: 6124: 6117: 6109: 6105: 6101: 6097: 6093: 6089: 6085: 6081: 6077: 6073: 6066: 6051: 6045: 6040: 6039: 6030: 6022: 6020:9780262018258 6016: 6012: 6008: 6002: 5994: 5990: 5986: 5982: 5978: 5974: 5970: 5966: 5961: 5956: 5952: 5948: 5947: 5939: 5931: 5927: 5922: 5917: 5912: 5907: 5903: 5899: 5895: 5888: 5873: 5869: 5868: 5860: 5844: 5840: 5836: 5831: 5826: 5822: 5818: 5814: 5807: 5798: 5783: 5779: 5775: 5769: 5767: 5758: 5754: 5749: 5744: 5740: 5736: 5732: 5728: 5727: 5722: 5718: 5717:Altman, Naomi 5711: 5696: 5692: 5690:9780262016469 5686: 5682: 5678: 5671: 5657: 5653: 5646: 5631: 5627: 5621: 5606: 5602: 5596: 5587: 5582: 5575: 5560: 5556: 5549: 5541: 5537: 5533: 5531:0-7695-2545-8 5527: 5523: 5519: 5515: 5511: 5504: 5496: 5492: 5488: 5484: 5480: 5476: 5469: 5462: 5451: 5447: 5443: 5439: 5435: 5430: 5425: 5421: 5417: 5410: 5403: 5387: 5380: 5371: 5366: 5362: 5358: 5357: 5352: 5345: 5343: 5334: 5328: 5324: 5323: 5318: 5317:Norvig, Peter 5314: 5308: 5306: 5304: 5302: 5293: 5289: 5285: 5283:9781555446116 5279: 5275: 5268: 5259: 5254: 5250: 5246: 5239: 5224: 5220: 5216: 5210: 5197:on 2012-03-09 5196: 5192: 5190:9781402067082 5186: 5182: 5178: 5174: 5168: 5160: 5154: 5150: 5143: 5141: 5134: 5130: 5127: 5120: 5111: 5102: 5095: 5089: 5074: 5070: 5066: 5062: 5058: 5054: 5050: 5046: 5042: 5038: 5034: 5030: 5026: 5022: 5015: 5000: 4996: 4992: 4986: 4984: 4982: 4965: 4961: 4957: 4953: 4949: 4945: 4941: 4937: 4933: 4929: 4922: 4906: 4902: 4898: 4891: 4882: 4880: 4871: 4867: 4863: 4859: 4854: 4849: 4845: 4841: 4834: 4826: 4822: 4818: 4812: 4810: 4800: 4793: 4787: 4783: 4779: 4778:Bishop, C. M. 4773: 4771: 4769: 4760: 4756: 4751: 4746: 4741: 4736: 4732: 4728: 4724: 4717: 4715: 4706: 4702: 4698: 4694: 4689: 4684: 4680: 4676: 4672: 4665: 4650: 4646: 4642: 4636: 4628: 4622: 4618: 4614: 4610: 4604: 4600: 4599:Arthur Samuel 4594: 4590: 4581: 4578: 4576: 4573: 4571: 4568: 4566: 4565:Force control 4563: 4560: 4557: 4554: 4551: 4548: 4545: 4544: 4536: 4534: 4529: 4527: 4525: 4520: 4518: 4516: 4511: 4509: 4507: 4502: 4500: 4498: 4493: 4491: 4489: 4484: 4482: 4480: 4475: 4473: 4471: 4466: 4464: 4461: 4460: 4452: 4449: 4447: 4444: 4442: 4439: 4437: 4434: 4432: 4429: 4428: 4418: 4415: 4413: 4410: 4408: 4405: 4403: 4400: 4398: 4395: 4393: 4390: 4388: 4385: 4383: 4380: 4378: 4375: 4373: 4370: 4368: 4365: 4363: 4360: 4358: 4355: 4353: 4350: 4348: 4345: 4343: 4340: 4338: 4335: 4333: 4330: 4328: 4325: 4322: 4319: 4317: 4314: 4313: 4304: 4301: 4299: 4296: 4295: 4286: 4283: 4281: 4278: 4276: 4272: 4269: 4267: 4263: 4260: 4258: 4255: 4253: 4250: 4248: 4245: 4243: 4240: 4238: 4235: 4232: 4229: 4227: 4224: 4222: 4219: 4217: 4214: 4212: 4209: 4207: 4204: 4202: 4199: 4197: 4194: 4192: 4189: 4187: 4184: 4182: 4179: 4177: 4174: 4172: 4169: 4167: 4164: 4162: 4159: 4157: 4154: 4152: 4149: 4147: 4144: 4142: 4139: 4138: 4131: 4129: 4120: 4118: 4114: 4110: 4106: 4102: 4098: 4094: 4090: 4086: 4082: 4073: 4071: 4067: 4063: 4059: 4056:is a type of 4055: 4051: 4041: 4038: 4034: 4030: 4020: 4016: 4014: 4013: 4006: 4003: 3998: 3996: 3991: 3987: 3983: 3979: 3973: 3969: 3965: 3955: 3953: 3949: 3945: 3941: 3937: 3932: 3930: 3925: 3920: 3910: 3908: 3904: 3899: 3894: 3892: 3886: 3877: 3869: 3864: 3854: 3849: 3839: 3837: 3833: 3828: 3826: 3822: 3817: 3813: 3808: 3803: 3793: 3789: 3787: 3783: 3779: 3774: 3772: 3767: 3758: 3754: 3750: 3748: 3744: 3739: 3735: 3731: 3726: 3722: 3721:AT&T Labs 3718: 3717:Netflix Prize 3714: 3706: 3703: 3701: 3698: 3696: 3693: 3691: 3688: 3686: 3683: 3681: 3678: 3676: 3673: 3671: 3668: 3666: 3663: 3661: 3658: 3656: 3653: 3651: 3648: 3646: 3643: 3641: 3638: 3636: 3633: 3631: 3628: 3626: 3623: 3621: 3618: 3616: 3613: 3611: 3608: 3606: 3603: 3601: 3598: 3596: 3593: 3591: 3588: 3586: 3583: 3580: 3577: 3575: 3572: 3570: 3567: 3565: 3562: 3560: 3557: 3555: 3552: 3549: 3546: 3544: 3541: 3538: 3535: 3533: 3530: 3527: 3524: 3522: 3519: 3517: 3514: 3512: 3509: 3507: 3504: 3502: 3499: 3497: 3494: 3492: 3489: 3487: 3484: 3482: 3479: 3477: 3474: 3472: 3469: 3467: 3464: 3462: 3459: 3457: 3454: 3452: 3449: 3448: 3446: 3438: 3436: 3432: 3427: 3421: 3411: 3409: 3404: 3400: 3396: 3392: 3381: 3379: 3375: 3371: 3367: 3363: 3359: 3355: 3348: 3338: 3336: 3331: 3327: 3323: 3319: 3315: 3311: 3305: 3295: 3293: 3289: 3284: 3280: 3278: 3274: 3270: 3261: 3256: 3246: 3244: 3240: 3237:, are called 3236: 3232: 3228: 3224: 3220: 3216: 3212: 3203: 3198: 3188: 3186: 3182: 3178: 3174: 3170: 3166: 3162: 3158: 3154: 3145: 3140: 3130: 3128: 3124: 3123:Platt scaling 3120: 3116: 3112: 3111:probabilistic 3107: 3101: 3091: 3089: 3085: 3081: 3077: 3072: 3068: 3067:decision tree 3061: 3056: 3051: 3041: 3038: 3037:Deep learning 3034: 3032: 3028: 3024: 3020: 3016: 3012: 3008: 3004: 2999: 2996: 2992: 2989:neurons is a 2987: 2983: 2979: 2974: 2972: 2968: 2964: 2963:connectionist 2956: 2952: 2948: 2943: 2938: 2937:Deep learning 2932: 2922: 2920: 2915: 2912: 2909:is a type of 2908: 2894: 2892: 2888: 2887:philosophical 2884: 2879: 2875: 2871: 2867: 2862: 2860: 2856: 2852: 2847: 2843: 2839: 2837: 2833: 2829: 2825: 2821: 2816: 2814: 2810: 2806: 2802: 2798: 2794: 2790: 2786: 2712: 2682: 2681:point-of-sale 2678: 2674: 2669: 2667: 2663: 2657: 2655: 2649: 2643: 2633: 2632:(e.g. MAML). 2631: 2630:meta-learning 2627: 2618: 2614: 2610: 2608: 2604: 2600: 2594: 2584: 2582: 2577: 2575: 2570: 2566: 2562: 2561:sparse matrix 2558: 2552: 2542: 2538: 2535: 2534:Deep learning 2531: 2527: 2523: 2522:Sparse coding 2519: 2515: 2513: 2509: 2505: 2501: 2497: 2493: 2489: 2484: 2482: 2477: 2471: 2461: 2455: 2451: 2449: 2445: 2443: 2439: 2437: 2433: 2430:in situation 2429: 2428: 2427: 2425: 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1785: 1783: 1779: 1776:According to 1774: 1772: 1767: 1764: 1760: 1752: 1741: 1739: 1735: 1731: 1725: 1723: 1719: 1715: 1711: 1707: 1706:connectionism 1703: 1699: 1695: 1691: 1687: 1683: 1678: 1676: 1672: 1668: 1664: 1660: 1656: 1652: 1648: 1639: 1625: 1621: 1619: 1615: 1611: 1607: 1603: 1599: 1595: 1591: 1587: 1583: 1579: 1577: 1573: 1569: 1565: 1561: 1557: 1553: 1548: 1546: 1542: 1538: 1534: 1530: 1526: 1522: 1521: 1516: 1513:psychologist 1512: 1508: 1504: 1503:Arthur Samuel 1499: 1497: 1493: 1489: 1485: 1481: 1480:Arthur Samuel 1477: 1471: 1461: 1459: 1454: 1452: 1448: 1444: 1440: 1435: 1433: 1429: 1428:statistically 1425: 1421: 1417: 1413: 1409: 1405: 1400: 1398: 1394: 1390: 1386: 1382: 1378: 1374: 1370: 1366: 1362: 1351: 1346: 1344: 1339: 1337: 1332: 1331: 1329: 1328: 1321: 1318: 1317: 1311: 1310: 1303: 1300: 1298: 1295: 1293: 1290: 1288: 1285: 1284: 1281: 1276: 1275: 1268: 1265: 1263: 1260: 1258: 1255: 1253: 1250: 1248: 1244: 1241: 1239: 1236: 1234: 1231: 1229: 1226: 1225: 1222: 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Retrieved 4644: 4635: 4608: 4593: 4532: 4523: 4514: 4505: 4496: 4487: 4478: 4469: 4352:KXEN Modeler 4237:scikit-learn 4126: 4113:Quantization 4093:edge devices 4080: 4079: 4047: 4026: 4017: 4010: 4007: 3999: 3975: 3933: 3916: 3895: 3887: 3883: 3874: 3851: 3829: 3809: 3805: 3790: 3775: 3768: 3764: 3755: 3751: 3734:Vinod Khosla 3710: 3635:Optimization 3537:DNA sequence 3532:Data quality 3444: 3441:Applications 3423: 3387: 3350: 3307: 3285: 3281: 3266: 3208: 3185:kernel trick 3150: 3127:kernel trick 3103: 3084:real numbers 3080:conjunctions 3064: 3035: 3000: 2975: 2960: 2916: 2902: 2900: 2882: 2878:Ehud Shapiro 2863: 2840: 2817: 2670: 2658: 2651: 2624: 2615: 2611: 2596: 2573: 2554: 2539: 2516: 2504:autoencoders 2485: 2473: 2458: 2453: 2447: 2441: 2435: 2431: 2423: 2421: 2405: 2366: 2305: 2287: 2276: 2258: 2253: 2249: 2245: 2241: 2237: 2233: 2231: 2225: 2195: 2163: 2148: 2121: 2091: 2055: 2039: 2031: 2008: 2004: 1978: 1968: 1966: 1956: 1952: 1948:data science 1929: 1922:algorithms. 1917: 1903:optimization 1900: 1894: 1890: 1874: 1866: 1860: 1821: 1805: 1786: 1782:Hutter Prize 1775: 1768: 1756: 1726: 1679: 1663:other models 1644: 1622: 1605: 1601: 1597: 1593: 1589: 1585: 1580: 1552:punched tape 1549: 1541:Walter Pitts 1518: 1500: 1495: 1475: 1473: 1455: 1436: 1401: 1395:. Recently, 1393:instructions 1364: 1360: 1359: 1233:Chinese room 1122:Applications 1078: 847:PAC learning 534: 383: 378:Hierarchical 310: 264: 258: 55: 32: 13070:Cybernetics 12986:Video games 12966:Digital art 12723:Concurrency 12592:Data mining 12504:Probability 12244:Interpreter 12020:Categories 11968:Autoencoder 11923:Transformer 11791:Alex Graves 11739:OpenAI Five 11643:IBM Watsonx 11265:Convolution 11243:Overfitting 11026:. Pearson, 10766:18 November 10720:Basic Books 10663:Electronics 9872:17 November 9862:The Outline 9841:17 November 9831:The Outline 8766:VentureBeat 8597:: 163–176. 8174:10037/24073 8023:October 20, 7172:(3): 1–58. 5904:(11): 972. 5898:Diagnostics 5605:www.ibm.com 5508:D. 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