Creator:
Contributor:
Korbicz, Józef - red. ; Uciński, Dariusz - red.
Title:
A fuzzy if-then rule-based nonlinear classifier
Group publication title:
Subject and Keywords:
classifier design ; fuzzy if-then rules ; generalization control ; mixture of experts
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.
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
Date:
Resource Type:
Pages:
Source:
AMCS, volume 13, number 2 (2003) ; click here to follow the link