Learning Similarity Measures: A Formal View Based on a Generalized CBR Model

Armin Stahl

In: Proceedings of the 6th International Conference on Case-Based Reasoning (ICCBR). International Conference on Case-Based Reasoning (ICCBR), Chicago, Pages 507-521, LNCS, Vol. 3620, Springer, 8/2005.


Although similarity measures play a crucial role in CBR applications, clear methodologies for defining them have not been developed yet. One approach to simplify the definition of similarity measures involves the use of machine learning techniques. In this paper we investigate important aspects of these approaches in order to support a more goal-directed choice and application of existing approaches and to initiate the development of new techniques. This investigation is based on a novel formal generalization of the classic CBR cycle, which allows a more suitable analysis of the requirements, goals, assumptions and restrictions that are relevant for learning similarity measures.

Weitere Links

ICCBR05_Stahl.pdf (pdf, 229 KB ) ICCBR2005_Stahl_Poster.pdf (pdf, 66 KB )

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz