TY - INPR
A1 - Globig, Christoph
A1 - Wess, Stefan
T1 - Learning in Case-Based Classification Algorithms
N2 - While symbolic learning approaches encode the knowledge provided by the presentation of the cases explicitly into a symbolic representation of the concept, e.g. formulas, rules, or decision trees, case-based approaches describe learned concepts implicitly by a pair (CB; d), i.e. by a set CB of cases and a distance measure d. Given the same information, symbolic as well as the case-based approach compute a classification when a new case is presented. This poses the question if there are any differences concerning the learning power of the two approaches. In this work we will study the relationship between the case base, the measure of distance, 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 conjecture 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.
KW - Case-Based Classification Algorithms
Y1 - 1994
UR - https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/133
UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:386-kluedo-1048
N1 - The presented work was partly supported by the Deutsche Forschungsgemeinschaft, project IND-CBL.
ER -