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A fuzzy if-then rule-based nonlinear classifier

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Łęski J.

International Journal of Applied Mathematics and Computer Science

2003

tom 13

nr 2

215-223

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.

Ograniczanie wyników

1 International Journal of Applied Mathematics and Computer Science

1 Łęski J.

1 2003

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A fuzzy if-then rule-based nonlinear classifier