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A fuzzy classifier with adaptive learning of norm inducing matrix

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

A fuzzy classifier with adaptive learning of the volume of norm inducing matrix is proposed in this paper. The proposed fuzzy classifier improves the Gustafson-Kessel (GK) algorithm which assumes a fixed volume of the norm inducing matrix. An efficient approach based on gradient descent learning is proposed to recursively update the volume of norm inducing matrix. Mathematical analyses and computer simulations are made to show the effectiveness and efficiency of the proposed fuzzy classifier.

Original languageEnglish
Title of host publication2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07
Pages362-367
Number of pages6
DOIs
StatePublished - 2007
Event2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07 - London, United Kingdom
Duration: 15 Apr 200717 Apr 2007

Publication series

Name2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07

Conference

Conference2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07
Country/TerritoryUnited Kingdom
CityLondon
Period15/04/0717/04/07

Keywords

  • Decision region
  • Fuzzy c means
  • Fuzzy classifier
  • Pattern recognition

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