A tutorial on v-support vector machines

被引：322

作者：

Chen, PH

Lin, CJ

Schölkopf, B

机构：

[1] Max Planck Inst Biol Cybernet, Tubingen, Germany

[2] Natl Taiwan Univ, Dept Comp Sci & Informat Engn, Taipei 106, Taiwan

来源：

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY | 2005年 / 21卷 / 02期

关键词：

v-support vector machines; support vector regression; support vector implementation; statistical learning theory; positive definite kernels;

D O I：

10.1002/asmb.537

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), and kernel feature spaces. We place particular emphasis on a description of the so-called v-SVM, including details of the algorithm and its implementation, theoretical results, and practical applications. Copyright (c) 2005 John Wiley & Sons, Ltd.

引用

页码：111 / 136

页数：26

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