HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Chang, M.-W. Articles by Lin, C.-J. Search for Related Content PubMed PubMed Citation Articles by Chang, M.-W. Articles by Lin, C.-J.

Leave-One-Out Bounds for Support Vector Regression Model Selection

b6506056{at}csie.ntu.edu.tw, Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan

Chih-Jen Lin

cjlin{at}csie.ntu.edu.tw, Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan

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.

This article has been cited by other articles:

				L. Gunter and J. Zhu Efficient Computation and Model Selection for the Support Vector Regression Neural Comput., June 1, 2007; 19(6): 1633 - 1655. [Abstract] [Full Text] [PDF]