Optimizing similarity assessment in case-based reasoning
Autor(en): | Stahl, A. Gabel, T. |
Stichwörter: | Application domains; Case-Based Reasoning; Feature weights, Classification (of information); Learning systems; Optimization, Information retrieval | Erscheinungsdatum: | 2006 | Journal: | Proceedings of the National Conference on Artificial Intelligence | Volumen: | 2 | Startseite: | 1667 | Seitenende: | 1670 | Zusammenfassung: | The definition of accurate similarity measures is a key issue of every Case-Based Reasoning application. Although some approaches to optimize similarity measures automatically have already been applied, these approaches are not suited for all CBR application domains. On the one hand, they are restricted to classification tasks. On the other hand, they only allow optimization of feature weights. We propose a novel learning approach which addresses both problems, i.e. it is suited for most CBR application domains beyond simple classification and it enables learning of more sophisticated similarity measures. Copyright © 2006, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. |
Beschreibung: | Conference of 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06 ; Conference Date: 16 July 2006 Through 20 July 2006; Conference Code:68475 |
ISBN: | 9781577352815 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-33750725694&partnerID=40&md5=b65b2aed37392276c0b311bb43cbe9da |
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