Geodesic
A Fuzzy If-Then Rule-Based Nonlinear Classifier
International Journal of Applied Mathematics and Computer Science, Tome 13 (2003) no. 2, pp. 215-223.

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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.
Keywords: classifier design, fuzzy if-then rules, generalization control, mixture of experts
Mots-clés : informatyka 
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     author = {{\L}\k{e}ski, J.},
     title = {A {Fuzzy} {If-Then} {Rule-Based} {Nonlinear} {Classifier}},
     journal = {International Journal of Applied Mathematics and Computer Science},
     pages = {215--223},
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     number = {2},
     year = {2003},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/IJAMCS_2003_13_2_a10/}
}
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Łęski, J. A Fuzzy If-Then Rule-Based Nonlinear Classifier. International Journal of Applied Mathematics and Computer Science, Tome 13 (2003) no. 2, pp. 215-223. http://geodesic.mathdoc.fr/item/IJAMCS_2003_13_2_a10/