Comparison of learning criteria for fuzzy classifier

Authors

  • S. D. Shtovba

Keywords:

classification, fuzzy knowledge base, learning

Abstract

A new criterion for fuzzy classifier learning is proposed. The proposed criterion inherits the advantages of well-known learning criteria: misclassification rate and distance between fuzzy sets. Executed experiments show that learning with proposed criterion produces the fuzzy classifiers with the best misclassification rate.

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Published

2010-11-12

How to Cite

[1]
S. D. Shtovba, “Comparison of learning criteria for fuzzy classifier”, Вісник ВПІ, no. 6, pp. 84–91, Nov. 2010.

Issue

No 6 (2007)

Section

Information technologies and computer sciences

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