Kernel Machines and Additive Fuzzy Systems:
Classification and Function Approximation
Yixin Chen, James Z. Wang
The Pennsylvania State University, University Park, PA 16802
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
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,
Copyright 2002 IEEE.
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January 28, 2003