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Learning an Adaptive Dissimilarity Measure for Nearest Neighbour Classification
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).ORCID iD: 0000-0003-2185-8973
Department of Applied Electronics, Kaunas University of Technology Kaunas, Lithuania.
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
2003 (English)In: Neural computing & applications (Print), ISSN 0941-0643, E-ISSN 1433-3058, Vol. 11, no 3-4, p. 203-209Article in journal (Refereed) Published
Abstract [en]

In this paper, an approach to weighting features for classification based on the nearest-neighbour rules is proposed. The weights are adaptive in the sense that the weight values are different in various regions of the feature space. The values of the weights are found by performing a random search in the weight space. A correct classification rate is the criterion maximised during the search. Experimentally, we have shown that the proposed approach is useful for classification. The weight values obtained during the experiments show that the importance of features may be different in different regions of the feature space

Place, publisher, year, edition, pages
London: Springer , 2003. Vol. 11, no 3-4, p. 203-209
Keywords [en]
Classification, Clustering, Learning vector quantisation, Nearest neighbour, Neural network
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:hh:diva-211DOI: 10.1007/s00521-003-0356-1ISI: 000184615000009Scopus ID: 2-s2.0-0038792231Local ID: 2082/506OAI: oai:DiVA.org:hh-211DiVA, id: diva2:237389
Available from: 2006-11-24 Created: 2006-11-24 Last updated: 2018-01-13Bibliographically approved

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Verikas, Antanas

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf