Fuzzy Support Vector Machines Based on Collaborative Representation

被引：0
|
作者：
Liu, Kaimin [1 ]
Wu, Xiao-Jun [1 ]
机构：
[1] Jiangnan Univ, Sch IoT Engn, Wuxi, Peoples R China
来源：
2015 11TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION (ICNC) | 2015年
关键词：
fuzzy support vector machines; fuzzy membership functions; collaborative representation coefficients;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The design of fuzzy membership functions is the key to fuzzy support vector machines. The fuzzy membership functions of traditional fuzzy support vector machines are based on the distances between the samples and the class center. However, it is easily affected by the sample points distribution. To alleviate this problem, in this paper, we proposed a new design of fuzzy membership function. The new method calculated fuzzy membership values based on the collaborative representation coefficients among the sample points. Experimental results show that the new method achieves higher accuracy which verifies its effectiveness.
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页码：64 / 68
页数：5
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