Case-based and symbolic classification

A case study
  • Stefan Wess
  • Christoph Globig
Selected Papers Positioning Case-Based Reasoning

DOI: 10.1007/3-540-58330-0_78

Part of the Lecture Notes in Computer Science book series (LNCS, volume 837)
Cite this paper as:
Wess S., Globig C. (1994) Case-based and symbolic classification. In: Wess S., Althoff KD., Richter M.M. (eds) Topics in Case-Based Reasoning. EWCBR 1993. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence), vol 837. Springer, Berlin, Heidelberg

Abstract

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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Copyright information

© Springer-Verlag 1994

Authors and Affiliations

  • Stefan Wess
    • 1
  • Christoph Globig
    • 1
  1. 1.University of KaiserslauternKaiserslauternGermany

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