New support vector algorithms

We propose a new class of support vector algorithms for regression and clas sification. In these algorithms, a parameter nu lets one effectively contro l the number of support vectors. While this can be useful in its own right, the parameterization has the additional benefit of enabling us to eliminat e one of the other free parameters of the algorithm: the accuracy parameter epsilon in the regression case, and the regularization constant C in the c lassification case. We describe the algorithms, give some theoretical resul ts concerning the meaning and the choice of nu, and report experimental res ults.