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A unifying framework for concept-learning algorithms

Published online by Cambridge University Press:  07 July 2009

Luc de Raedt  and
Maurice Bruynooghe
Luc de Raedt
Affiliation:
Department of Computer Science, Katholleke Universiteit Leuven, Celestijnenlaan 200A, B-3001 Heverlee, Belgium
Maurice Bruynooghe
Affiliation:
Department of Computer Science, Katholleke Universiteit Leuven, Celestijnenlaan 200A, B-3001 Heverlee, Belgium
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Abstract

A unifying framework for concept-learning, derived from Mitchell's Generalization as Search-paradigm, is presented. Central to the framework is the generic algorithm Gencol. Gencol forms a synthesis of existing concept-learning algorithms as it identifies the key issues in concept-learning: the representation of concepts and examples, the search strategy and heuristics, and the operators that transform one concept-description into another one when searching the concept description space. Gencol is relevant for practical purposes as it offers a solid basis for the design and implementation of concept-learning algorithms. The presented framework is quite general as seemingly disparate algorithms such as TDIDT, AQ, MIS and version spaces fit into Gencol.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 7 , Issue 3 , September 1992 , pp. 251 - 269
Copyright
Copyright © Cambridge University Press 1992

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