Leave-one-out bounds for support vector regression model selection

被引：79

作者：

Chang, MW ^{[1
]}

Lin, CJ ^{[1
]}

机构：

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

来源：

NEURAL COMPUTATION | 2005年 / 17卷 / 05期

关键词：

D O I：

10.1162/0899766053491869

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Minimizing bounds of leave-one-out errors is an important and efficient approach for support vector machine (SVM) model selection. Past research focuses on their use for classification but not regression. In this letter, we derive various leave-one-out bounds for support vector regression (SVR) and discuss the difference from those for classification. Experiments demonstrate that the proposed bounds are competitive with Bayesian SVR for parameter selection. We also discuss the differentiability of leave-one-out bounds.

引用

页码：1188 / 1222

页数：35

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