The new interpretation of support vector machines on statistical learning theory

被引：32
|
作者：
Zhang ChunHua [2 ]
Tian YingJie [3 ]
Deng NaiYang [1 ]
机构：
[1] China Agr Univ, Coll Sci, Beijing 100083, Peoples R China
[2] Renmin Univ China, Sch Informat, Beijing 100872, Peoples R China
[3] Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100080, Peoples R China
来源：
SCIENCE CHINA-MATHEMATICS | 2010年 / 53卷 / 01期
基金：
中国国家自然科学基金;
关键词：
C-support vector classification; the minimization principle of the structural risk; KKT conditions; CLASSIFICATION; CONSISTENCY;
D O I：
10.1007/s11425-010-0018-6
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This paper is concerned with the theoretical foundation of support vector machines (SVMs). The purpose is to develop further an exact relationship between SVMs and the statistical learning theory (SLT). As a representative, the standard C-support vector classification (C-SVC) is considered here. More precisely, we show that the decision function obtained by C-SVC is just one of the decision functions obtained by solving the optimization problem derived directly from the structural risk minimization principle. In addition, an interesting meaning of the parameter C in C-SVC is given by showing that C corresponds to the size of the decision function candidate set in the structural risk minimization principle.
引用
收藏
页码：151 / 164
页数：14
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