Properties of learning of a fuzzy ART variant

M. Georgiopoulos, I. Dagher, G. L. Heileman, G. Bebis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This paper discusses one variation of the fuzzy ART architecture, referred to as fuzzy ART variant. The fuzzy ART variant is a fuzzy ART algorithm, with a very large value for the choice parameter. Based on the geometrical interpretation of templates in fuzzy ART we present and prove useful properties of learning pertaining to the fuzzy ART variant. One of these properties of learning establishes an upper bound on the number of list presentations required by the fuzzy ART variant to learn an arbitrary list of input patterns presented to it. In previously published work, it was shown that the fuzzy ART variant performs as well as a fuzzy ART algorithm with more typical values for the choice parameter. Hence, the fuzzy ART variant is as good a clustering machine as the fuzzy ART algorithm using more typical values of the choice parameter.

Original languageEnglish (US)
Title of host publication1997 IEEE International Conference on Neural Networks, ICNN 1997
Pages2012-2016
Number of pages5
DOIs
StatePublished - 1997
Externally publishedYes
Event1997 IEEE International Conference on Neural Networks, ICNN 1997 - Houston, TX, United States
Duration: Jun 9 1997Jun 12 1997

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
Volume3
ISSN (Print)1098-7576

Conference

Conference1997 IEEE International Conference on Neural Networks, ICNN 1997
Country/TerritoryUnited States
CityHouston, TX
Period6/9/976/12/97

ASJC Scopus subject areas

  • Software

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