The evidence framework applied to support vector machines

被引：110

作者：

Kwok, JTY ^{[1
]}

机构：

[1] Hong Kong Baptist Univ, Dept Comp Sci, Hong Kong, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2000年 / 11卷 / 05期

关键词：

Bayesian inference; evidence framework; support vector machine (SVM);

D O I：

10.1109/72.870047

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we show that training of the support vector machine (SVM) can be interpreted as performing the level 1 inference of MacKay's evidence framework. We further on show that levels 2 and 3 of the evidence framework can also be applied to SVMs. This integration allows automatic adjustment of the regularization parameter and the kernel parameter to their near-optimal values, Moreover, it opens up a wealth of Bayesian tools for use with SVMs. Performance of this method is evaluated on both synthetic and real-world data sets.

引用

页码：1162 / 1173

页数：12

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