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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.

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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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