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.


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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.

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Last Modified: January 28, 2003
2003