On the usefulness of fuzzy SVMs and the extraction of fuzzy rules from SVMs

被引：0

作者：

Moewes, Christian ^{[1
]}

Kruse, Rudolf ^{[1
]}

机构：

[1] Univ Magdeburg, Fac Comp Sci, D-39106 Magdeburg, Germany

来源：

PROCEEDINGS OF THE 7TH CONFERENCE OF THE EUROPEAN SOCIETY FOR FUZZY LOGIC AND TECHNOLOGY (EUSFLAT-2011) AND LFA-2011 | 2011年

关键词：

Classification; fuzzy rule-based classifiers; fuzzy SVM; SVM; SUPPORT; NETWORK;

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：

In this paper we reason about the usefulness of two recent trends in fuzzy methods in machine learning. That is, we discuss both fuzzy support vector machines (FSVMs) and the extraction of fuzzy rules from SVMs. First, we show that an FSVM is identical to a special type of SVM. Second, we categorize and analyze existing approaches to obtain fuzzy rules from SVMs. Finally, we question both trends and conclude with more promising alternatives.

引用

页码：943 / 948

页数：6

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