Radius-margin bound on the leave-one-out error of a M-SVM
Yann Guermeur and Emmanuel Monfrini
(2009) Technical Report. LORIA, Nancy, France.

Abstract

Using a support vector machine (SVM) requires to set the values of two types of hyperparameters: the soft margin parameter C and the parameters of the kernel. To perform this model selection task, the method of choice is cross-validation. Its leave-one-out variant is known to produce an estimator of the generalization error which is almost unbiased. Its major drawback rests in its time requirement. To overcome this difficulty, several upper bounds on the leave-one-out error of the pattern recognition SVM have been derived. Among those bounds, the most popular one is probably the radius-margin bound. In this report, we establish a generalized radius-margin bound dedicated to the multi-class SVM of Lee, Lin and Wahba.

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