A new method for multiclass support vector machines

In this paper we present a new method for solving multiclass problems with a support vector machine. Our method compares favorably with other proposals, appeared so far in the literature, both in terms of computational needs for the feedforward phase and of classification accuracy. The main result, however, is the mapping of the multiclass problem to a biclass one, which allows us to suggest a method for estimating the generalization error by using data-dependent error bounds.