An New Fuzzy Support Vector Machine for Binary Classification

被引：1
|
作者：
Zhang, Rui [1 ]
Liu, Tongbo [1 ]
Zheng, Mingwen [1 ]
机构：
[1] Shandong Univ Technol Zibo, Sch Sci, Jinan, Peoples R China
来源：
MATERIALS SCIENCE AND INFORMATION TECHNOLOGY, PTS 1-8 | 2012年 / 433-440卷
关键词：
SVM; FSVM; kernel function;
D O I：
10.4028/www.scientific.net/AMR.433-440.2856
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper, we proposed a new fuzzy support vector machine(called L2-FSVM here), which error part of object is L2-norm.Meanwhile we introduce a new method of generating fuzzy memberships so as to reduce to effects of outliers. The experimental results demonstrate that the L2-FSVM method provides improved ability to reduce to effects of outliers in comparison with traditional SVMs and FSVMs, and claim that L2-FSVM is the best way to solve the binary classification in the three methods stated above.
引用
收藏
页码：2856 / 2861
页数：6
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