|Title:||Featureless Similarity||Authors:||Cunningham, Pádraig; Delany, Sarah Jane||Permanent link:||http://hdl.handle.net/10197/12358||Date:||23-Feb-2007||Online since:||2021-07-29T15:55:47Z||Abstract:||Assessing the similarity between cases is a key aspect of the retrieval phase in Case-Based Reasoning (CBR). In most CBR work, similarity is assessed based on feature-value descriptions of cases using similarity metrics which use these feature values. In fact it might be said that this notion of a feature-value representation is a defining part of the CBR world-view – it underpins the idea of a problem space with cases located relative to each other in this space. Recently a variety of similarity mechanisms have emerged that are not feature-based. Some of these ideas have emerged in CBR research but many of them have arisen in other areas of data analysis. In fact research on Support Vector Machines(SVM) is a rich source of novel similarity representations because of the emphasis on encoding domain knowledge in the kernel function of the SVM. In this paper we review these novel featureless similarity measures and assess the implications these measures have for CBR research.||Type of material:||Technical Report||Publisher:||University College Dublin. School of Computer Science and Informatics||Series/Report no.:||UCD CSI Technical Reports; UCD-CSI-2007-1||Copyright (published version):||2007 the Authors||Keywords:||Case-based reasoning; Machine learning; Feature-based similarity||Other versions:||https://web.archive.org/web/20080226040105/http:/csiweb.ucd.ie/Research/TechnicalReports.html||Language:||en||Status of Item:||Not peer reviewed||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Computer Science and Informatics Technical Reports|
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