Kernel machines and additive fuzzy systems: Classification and function approximation

被引：0

作者：

Chen, YX ^{[1
]}

Wang, JZ ^{[1
]}

机构：

[1] Penn State Univ, Dept Comp Sci & Engn, University Pk, PA 16802 USA

来源：

PROCEEDINGS OF THE 12TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1 AND 2 | 2003年

关键词：

puzzy systems; support vector machines; support vector regression;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：789 / 795

页数：7

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