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Abstract

This book presents a theory of learning from examples called Nested Generalized Exemplar (NGE) theory, and demonstrates its importance with empirical results in several domains. Nested Generalized Exemplar theory is a variation of a learning model called exemplar-based learning, which was originally proposed as a model of human learning by Medin and Schaffer [1978]. In the simplest form of exemplar-based learning, every example is stored in memory verbatim, with no change of representation. The set of examples that accumulate over time form category definitions; for example, the set of all chairs that a person has seen forms that person’s definition of “chair.” An example is normally defined as a vector of features, with values for each feature, plus a label which represents the category of the example.

Keywords

Learning System Learning Program Concept Learning Disjunctive Normal Form Prediction Failure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 1990

Authors and Affiliations

  • Steven L. Salzberg
    • 1
  1. 1.The Johns Hopkins UniversityUSA

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