A fuzzy if-then rule-based nonlinear classifier

Jacek Łęski

International Journal of Applied Mathematics and Computer Science (2003)

  • Volume: 13, Issue: 2, page 215-223
  • ISSN: 1641-876X

Abstract

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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.

How to cite

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Łęski, Jacek. "A fuzzy if-then rule-based nonlinear classifier." International Journal of Applied Mathematics and Computer Science 13.2 (2003): 215-223. <http://eudml.org/doc/207638>.

@article{Łęski2003,
abstract = {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.},
author = {Łęski, Jacek},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {classifier design; mixture of experts; fuzzy if-then rules; generalization control},
language = {eng},
number = {2},
pages = {215-223},
title = {A fuzzy if-then rule-based nonlinear classifier},
url = {http://eudml.org/doc/207638},
volume = {13},
year = {2003},
}

TY - JOUR
AU - Łęski, Jacek
TI - A fuzzy if-then rule-based nonlinear classifier
JO - International Journal of Applied Mathematics and Computer Science
PY - 2003
VL - 13
IS - 2
SP - 215
EP - 223
AB - 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.
LA - eng
KW - classifier design; mixture of experts; fuzzy if-then rules; generalization control
UR - http://eudml.org/doc/207638
ER -

References

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