Kernel Machines and Additive Fuzzy Systems:
Classification and Function Approximation

Yixin Chen, James Z. Wang
The Pennsylvania State University, University Park, PA 16802
Abstract:

This paper investigates the connection between additive fuzzy systems and kernel machines. We prove that, under quite general conditions, these two seemingly quite distinct models are essentially equivalent. As a result, algorithms based upon Support Vector (SV) learning are proposed to build fuzzy systems for classification and function approximation. The performance of the proposed algorithm is illustrated using extensive experimental results.


Full Paper in Color

(PDF, 0.4MB)

More Info


Citation: Yixin Chen and James Z. Wang, ``Kernel Machines and Additive Fuzzy Systems: Classification and Function Approximation,'' Proc. IEEE International Conference on Fuzzy Systems, pp. 789-795, St. Louis, MO, 2003.

Copyright 2002 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the IEEE.

Last Modified: January 28, 2003
© 2003