Unlabeled patterns to tighten Rademacher complexity error bounds for kernel classifiers
New upper-bounds for generalization error estimation of classifiers are proposed. They can be used for effective in-sample model selection purposes. The bounds base on the Rademacher complexity and are useful when unlabeled patterns are available. Confidence term in the Rademacher bound can be reduced by a factor of three. Localized versions of the hypothesis class are derived, allowing to tighten the error estimation.
rademacher complexity error estimation model selection support vector machine structural risk minimization