Using membership functions to improve multi-class SVM classification

被引：0

作者：

Wang, XD ^{[1
]}

Wu, CM ^{[1
]}

机构：

[1] AF Engn Univ, Missile Inst, San Yuan 713800, Shaanxi, Peoples R China

来源：

2004 7TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, VOLS 1-3 | 2004年

关键词：

support vector machine; multi-class classification;

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：

Support vector machine is originally designed for binary classification. One-agamst-one method is commonly used in multi-class classification. Based on the analysis of the decision process of max wins used in one-against-one method, new membership functions are introduced to resolve the possibly existed unclassifiable regions, and a fuzzy SVM multi-class classification algorithm FSVM is proposed. Classification experiments result proves the effectiveness of FSVM.

引用

页码：1459 / 1462

页数：4

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