Case-Based and Symbolic Classification*
- A Case Study -

Stefan Wess and Christoph Globig

University of Kaiserslautern, P.O. Box 3049
D-67653 Kaiserslautern, Germany
{globig,wess}@informatik.uni-kl.de



Abstract. Contrary to symbolic learning approaches, that represent a
learned concept explicitly, case-based approaches describe concepts implicitly
 by a pair (GB, 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 case-based 
methods and show the interdependency between the measure used
by a case-based algorithm and the target concept.
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