A moment inequality for multicategory support vector machines

The success of support vector machines in binary classification relies on the fact that hinge loss utilized in the risk minimization targets the Bayes rule. Recent research explores some extensions of this large margin based method to the multicategory case. We obtain a moment inequality for multicategory support vector machine loss minimizers with fast rate of convergence.

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