Refereed full papers (journals, book chapters, international conferences)

2004

@inproceedings{AEG2004,
	vgclass =	{refpap},
	author =	{Eva Armengol and Francesc Esteva and Llu\`{\i}s Godo and
	Vicen\c{c} Torra},
	title =	{On Learning Similarity Relations in Fuzzy Case-Based
	Reasoning},
	booktitle =	{Transactions on Rough Sets {II}: Rough Sets and Fuzzy Sets},
	number =	{3135},
	series =	{Lecture Notes in Computer Science},
	pages =	{14--32},
	publisher =	{Springer-Verlag},
	year =	{2004},
	url =	{http://www.springerlink.com/link.asp?id=n5lq30gdb9km0n7c},
	abstract =	{Case-based reasoning (CBR) is a problem solving technique
	that puts at work the general principle that similar problems have
	similar solutions. In particular, it has been proved effective for
	classification problems. Fuzzy set-based approaches to CBR rely on the
	existence of a fuzzy similarity functions on the problem description
	and problem solution domains. In this paper, we study the problem of
	learning a global similarity measure in the problem description domain
	as a weighted average of the attribute-based similarities and,
	therefore, the learning problem consists in finding the weighting
	vector that minimizes mis-classification. The approach is validated by
	comparing results with an application of case-based reasoning in a
	medical domain that uses a different model.},
}