SVM classification: Its contents and challenges

被引：73

作者：

Shihong Yue

Ping Li

Peiyi Hao

机构：

[1] Institute of Industrial Process Control, Zhejiang Univ, Hangzhou

[2] Dept. of Computer Sci. and inform. Eng, Cheng Kung Univ

来源：

Applied Mathematics-A Journal of Chinese Universities | 2003年 / 18卷 / 3期

关键词：

Kernel methods; Mathematical programming; SVM;

D O I：

10.1007/s11766-003-0059-5

中图分类号：

学科分类号：

摘要：

SVM (support vector machines) have become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. In particular, they exhibit good generalization performance on many real issues and the approach is properly motivated theoretically. There are relatively a few free parameters to adjust and the architecture of the learning machine does not need to be found by experimentation. In this paper, survey of the key contents on this subject, focusing on the most well-known models based on kernel substitution, namely SVM, as well as the activated fields at present and the development tendency, is presented. © 2003 Springer Verlag.

引用

页码：332 / 342

页数：10

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