The generalization error of the symmetric and scaled support vector machines

被引：12

作者：

Feng, JF ^{[1
]}

Williams, P ^{[1
]}

机构：

[1] Univ Sussex, Sch Cognit & Comp Sci, Brighton BN1 9QH, E Sussex, England

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2001年 / 12卷 / 05期

关键词：

generalization error; scaled SVM; SVM (SVM);

D O I：

10.1109/72.950155

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

It is generally believed that the support vector machine (SVM) optimizes the generalization error and outperforms other learning machines. We show analytically, by concrete examples in the one dimensional case, that the SVM does improve the mean and standard deviation of the generalization error by a constant factor, compared to the worst learning machine. Our approach is in terms of extreme value theory and both the mean and variance of the generalization error are calculated exactly for all cases considered. We propose a new version of the SVM (scaled SVM) which can further reduce the mean of the generalization error of the SVM.

引用

页码：1255 / 1260

页数：6

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