  We consider a problem of risk estimation for large-margin multi-class
classifiers. We propose a novel risk bound for the multi-class classification
problem. The bound involves the marginal distribution of the classifier and the
Rademacher complexity of the hypothesis class. We prove that our bound is tight
in the number of classes. Finally, we compare our bound with the related ones
and provide a simplified version of the bound for the multi-class
classification with kernel based hypotheses.
