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

Armin Stahl
Proceedings of the 6th International Conference on Case-Based Reasoning (ICCBR) volume 3620, LNCS, Pages 507-521, Chicago, Springer, Chicago, IL, 8/2005

Abstract:

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.

Files:

  http://www.springerlink.com/content/2n43ftwl7hfkjmrh/
  ICCBR05_Stahl.pdf
  ICCBR2005_Stahl_Poster.pdf

BibTex:

@inproceedings{ STAH2005,
	Title = {Learning Similarity Measures: A Formal View Based on a Generalized CBR Model},
	Author = {Armin Stahl},
	BookTitle = {Proceedings of the 6th International Conference on Case-Based Reasoning (ICCBR)},
	Month = {8},
	Year = {2005},
	Series = {LNCS},
	Publisher = {Springer},
	Publisher = {3620},
	Pages = {507-521},
	Address = {Chicago, IL}
}

     
Last modified:: 30.08.2016