Case-Based and Symbolic Classification Algorithms? - A Case Study using Version Space

  • Contrary to symbolic learning approaches, that represent a learned concept explicitly, case-based approaches describe concepts implicitly by a pair (CB; sim), i.e. by a measure of similarity sim and a set CB of cases. This poses the question if there are any differences concerning the learning power of the two approaches. In this article we will study the relationship between the case base, the measure of similarity, and the target concept of the learning process. To do so, we transform a simple symbolic learning algorithm (the version space algorithm) into an equivalent case-based variant. The achieved results strengthen the hypothesis of the equivalence of the learning power of symbolic and casebased methods and show the interdependency between the measure used by a case-based algorithm and the target concept.

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Metadaten
Author:Christoph Globig, Stefan Wess
URN (permanent link):urn:nbn:de:hbz:386-kluedo-995
Document Type:Preprint
Language of publication:English
Year of Completion:1998
Year of Publication:1998
Publishing Institute:Technische Universität Kaiserslautern
Note:
The presented work was partly supported by the Deutsche Forschungsgemeinschaft, SFB 314: "Artificial Intelligence and Knowledge-Based Systems" and the project IND-CBL.
Faculties / Organisational entities:Fachbereich Informatik
DDC-Cassification:004 Datenverarbeitung; Informatik

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