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Concept learning refers to a learning task in which a human or machine learner is trained to classify objects by being shown a set of example objects along with their class labels. In the machine learning literature, this task is more typically called supervised learning or supervised classification, in contrast to unsupervised learning or unsupervised classification, in which the learner is not provided with class labels. Colloquially, this task is known as learning from examples. As a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques that allow computers to learn. At a general level, there are two types of learning: inductive, and deductive. ...
Supervised learning is a machine learning technique for creating a function from training data. ...
Unsupervised learning is a method of machine learning where a model is fit to observations. ...
The Theoretical Issues
The theoretical issues underlying concept learning are those underlying induction in general. These issues are addressed in many diverse literatures, including Version Spaces, Statistical Learning Theory, PAC Learning, Information Theory, and Algorithmic Information Theory. Some of the broad theoretical ideas are also discussed by Watanabe (1969,1985), Solomonoff (1964a,1964b), and Rendell (1986). Look up induction in Wiktionary, the free dictionary. ...
A version space in concept learning or induction is the subset of all hypotheses that are consistent with the observed training examples (Mitchell 1997). ...
Statistical learning theory is an ambiguous term. ...
Probably approximately correct learning (PAC learning) is a framework of learning that was proposed by Leslie Valiant in his paper A theory of the learnable. ...
Not to be confused with information technology, information science, or informatics. ...
This article or section is in need of attention from an expert on the subject. ...
Modern Psychological Theories of Concept Learning It is difficult to make any general statements about human (or animal) concept learning without already assuming a particular psychological theory of concept learning. Although the classical views of concepts and concept learning in philosophy speak of a process of abstraction, data compression, simplification, and summarization, currently popular psychological theories of concept learning diverge on all these basic points. A very insightful but easy-to-grasp start is provided here A concept is an abstract, universal psychical entity that serves to designate a category or class of entities, events or relations. ...
abstraction in general. ...
“Source coding†redirects here. ...
Rule-Based Theories of Concept Learning To be added....
The prototype view on concept learning holds that people abstract out the central tendency (or prototype) of the experienced examples, and use this as a basis for their categorization decisions. Prototype Theory is a model of graded categorization in Cognitive Science, where some members of a category are more central than others. ...
Exemplar Theories of Concept Learning Exemplar theories of concept learning are those in which the learner is hypothesized to store the provided training examples verbatim, without creating any abstraction or reduced representation (e.g., rules). In machine learning, algorithms of this type are also known as instance learners or lazy learners. The best known exemplar theory of concept learning is the Generalized Context Model (GCM), Nosofsky's (1986) generalization of Medin ans Schaffer's (1978) Context Model. A connectionist version of the GCM, called ALCOVE, has been developed by Kruschke (1992). Connectionism today generally refers to an approach in the fields of cognitive psychology, cognitive science and philosophy of mind which models mental or behavioral phenomena with neural networks, and is associated with a certain set of arguments for why this is a good idea. ...
Explanation-Based Theories of Concept Learning
Bayesian Theories of Concept Learning Bayesian theories are those which directly apply normative probability theory to achieve optimal learning. They generally base their categorization of data on the posterior probability for each category, where for category i, this posterior is given by Bayes rule, 
where P(D | Ci) is the probability of observing the given data on the assumption it was generated from category Ci, P(Ci) is the prior probability of category Ci, and P(D) is the marginal probability of observing the data, which usually does not enter into consideration. In general, the category possessing the maximum posterior P(Ci | D) would be category selected for the given data. The details of these methods can be fairly complex, as they require assumptions about the prior probability of categories and the generation of data from the categories.
The best known Bayesian theory of concept learning is the rational model of categorization, developed by John R. Anderson (1991). Other approaches have been offered by Joshua Tenenbaum. John Robert Anderson (born 1947 in Vancouver, British Columbia) is a professor of psychology and computer science at Carnegie Mellon University. ...
Intermediate Theories of Concept Learning To be added....
Machine Learning Approaches to Concept Learning Unlike the situation in Psychology, the problem of concept learning within machine learning is not one of finding the "right" theory of concept learning, but one of finding the most effective method for a given task. As such, there has been a huge proliferation of concept learning theories. Here we simply list a sampling: As a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques that allow computers to learn. At a general level, there are two types of learning: inductive, and deductive. ...
See Also For Wikipedias categorization projects, see Wikipedia:Categorization. ...
Look up induction in Wiktionary, the free dictionary. ...
Rule induction is an area of machine learning in which formal rules are extracted from a set of observations. ...
References - Rendell, Larry (1986). "A general framework for induction and a study of selective induction". Machine Learning 1: 177–226.
- Watanabe, Satosi (1969). Knowing and Guessing: A Quantitative Study of Inference and Information. New York: Wiley.
- Watanabe, Satosi (1985). Pattern Recognition: Human and Mechanical. New York: Wiley.
- Solomonoff, R. J. (1964). "A formal theory of inductive inference. Part I". Information and Control 7 (1): 1–22.
- Solomonoff, R. J. (1964). "A formal theory of inductive inference. Part II". Information and Control 7 (2): 224–254.
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