Evaluation of simple performance measures for tuning SVM hyperparameters

• Published in: Neurocomputing (Amsterdam) Vol. 51; pp. 41 - 59
• Main Authors: Duan, Kaibo, Keerthi, S.Sathiya, Poo, Aun Neow
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2003
• Subjects: Generalization error bound; Model selection; SVM; Model selection; Generalization error bound; SVM
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/S0925-2312(02)00601-X

Choosing optimal hyperparameter values for support vector machines is an important step in SVM design. This is usually done by minimizing either an estimate of generalization error or some other related performance measure. In this paper, we empirically study the usefulness of several simple performance measures that are inexpensive to compute (in the sense that they do not require expensive matrix operations involving the kernel matrix). The results point out which of these measures are adequate functionals for tuning SVM hyperparameters. For SVMs with L1 soft-margin formulation, none of the simple measures yields a performance uniformly as good as k-fold cross validation; Joachims’ Xi-Alpha bound and the GACV of Wahba et al. come next and perform reasonably well. For SVMs with L2 soft-margin formulation, the radius margin bound gives a very good prediction of optimal hyperparameter values.