A fuzzy if-then rule-based nonlinear classifier

• Autorzy: Jacek Łęski
• Języki publikacji: EN

Abstrakty

EN

This paper introduces a new classifier design method that is based on a modification of the classical Ho-Kashyap procedure. The proposed method uses the absolute error, rather than the squared error, to design a linear classifier. Additionally, easy control of the generalization ability and robustness to outliers are obtained. Next, an extension to a nonlinear classifier by the mixture-of-experts technique is presented. Each expert is represented by a fuzzy if-then rule in the Takagi-Sugeno-Kang form. Finally, examples are given to demonstrate the validity of the introduced method.

Słowa kluczowe

EN

classifier design; mixture of experts; fuzzy if-then rules; generalization control

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2003
• Tom: 13
• Numer: 2
• Strony: 215-223

Daty

wydano

2003

otrzymano

2002-05-28

poprawiono

2002-08-29

Twórcy

autor

Jacek Łęski

• Institute of Electronics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland

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