Exploiting Background Knowledge when Learning Similarity Measures

Thomas Gabel, Armin Stahl

In: Proceedings of the 7th European Conference on Case-Based Reasoning ECCBR'04. European Conference on Case-Based Reasoning (ECCBR) Springer 2004.


The definition of similarity measures - one core component of each CBR application - leads to a serious knowledge acquisition problem if domain and application specific requirements have to be considered. To reduce the knowledge acquisition effort, different machine learning techniques have been developed in the past. In this paper, enhancements of our framework for learning knowledge-intensive similarity measures are presented. The described techniques aim on a restriction of the search space to be considered by the learning algorithm by exploiting available background knowledge.

Weitere Links

ECCBR2004_Gabel_Stahl.pdf (pdf, 198 KB ) ECCBR2004_Gabel_Stahl_Slides.pdf (pdf, 533 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence