Kernel machines and additive fuzzy systems: Classification and function approximation

Yixin Chen; James Z. Wang

Kernel machines and additive fuzzy systems: Classification and function approximation

Research output: Contribution to conference › Paper › peer-review

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.

Original language	English (US)
Pages	789-795
Number of pages	7
State	Published - 2003
Event	The IEEE International conference on Fuzzy Systems - St. Louis, MO, United States Duration: May 25 2003 → May 28 2003

Other

Other	The IEEE International conference on Fuzzy Systems
Country/Territory	United States
City	St. Louis, MO
Period	5/25/03 → 5/28/03

All Science Journal Classification (ASJC) codes

Software
Theoretical Computer Science
Artificial Intelligence
Applied Mathematics

Cite this

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title = "Kernel machines and additive fuzzy systems: Classification and function approximation",

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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T2 - The IEEE International conference on Fuzzy Systems

AU - Chen, Yixin

AU - Wang, James Z.

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N2 - 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.

AB - 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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Y2 - 25 May 2003 through 28 May 2003

ER -

Kernel machines and additive fuzzy systems: Classification and function approximation

Abstract

Other

All Science Journal Classification (ASJC) codes

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