Featureless Similarity

Files in This Item:
 File SizeFormat
DownloadUCD-CSI-2007-1.pdf338.52 kBAdobe PDF
Title: Featureless Similarity
Authors: Cunningham, PádraigDelany, 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 reasoningMachine learningFeature-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Status of Item: Not peer reviewed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

Show full item record

Page view(s)

Last Week
Last month
checked on Sep 20, 2021


checked on Sep 20, 2021

Google ScholarTM


If you are a publisher or author and have copyright concerns for any item, please email research.repository@ucd.ie and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.