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Quality function for unsupervised classification and its use in graphic arts
Kaunas University of Technology, Kaunas, Lithuania.
Kaunas University of Technology, Studentu 50 3031, Kaunas, Lithuania.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
1999 (English)In: Journal of Advanced Computational Intelligence, Vol. 3, no 6, p. 532-540Article in journal (Refereed) Published
Abstract [en]

In this paper, we propose quality function for an unsupervised neural classification. The function is based on the third order polynomials. The objective of the quality function is to find a place of the input space sparse in data points. By maximising the quality function, we find decision boundary between data clusters instead of centres of the clusters. The shape and place of the decision boundary are rather insensitive to the magnitude of the weight vector established during the maximisation process. A superiority of the proposed quality function over other similar functions as well as conventional clustering algorithms tested has been observed in the experiments. The proposed quality function has been successfully used for colour image segmentation.

Place, publisher, year, edition, pages
Japan, 1999. Vol. 3, no 6, p. 532-540
Keywords [en]
Optimization, Printing quality
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hh:diva-21501OAI: oai:DiVA.org:hh-21501DiVA, id: diva2:605953
Available from: 2013-02-16 Created: 2013-02-16 Last updated: 2018-03-22Bibliographically approved

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Verikas, Antanas

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