Analysis of support vector machine classification

被引：0

作者：

Wu, QA ^{[1
]}

Zhou, DX ^{[1
]}

机构：

[1] City Univ Hong Kong, Dept Math, Kowloon, Hong Kong, Peoples R China

来源：

JOURNAL OF COMPUTATIONAL ANALYSIS AND APPLICATIONS | 2006年 / 8卷 / 02期

关键词：

support vector machine classification; misclassification error; bayesrisk consistency; consistency with hypothesis space; mercer kernel; regularization error;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper studies support vector machine classification algorithms. We analyze the 1-norm soft margin classifier. The consistency is considered in two forms. When the regularization error decays to zero, the Bayes-risk consistency is proved and learning rates are derived by means of techniques of uniform convergence. The main difficulty we overcome here is to bound the offset. For the consistency with hypothesis space, we present a counterexample.

引用

页码：99 / 119

页数：21

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