Bound on the Leave-One-Out Error for -SVMs
A bound on the leave-one-out error for -support vector (SV) machine binary classifiers is presented. This bound is based on the ge ometrical concept of thespan, which was introduced in the context of bounding the leave-one-out error for C-SV machine binary classifiers. It is shown that the bound presented herein requires less restrictive a ssumptions than theC-SV bound, and that the prediction of generalisation perfor mance is improved, in particular for noisy data sets.