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

Authors:
Ming-Wei Chang;Chih-Jen Lin
Affiliations:
Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan;Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan
Venue:
Neural Computation
Year:
2005

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Abstract

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.