Efficiency of classification methods based on empirical risk minimization

A binary classification problem is reduced to the minimization of convex regularized empirical risk functionals in a reproducing kernel Hilbert space. The solution is searched for in the form of a finite linear combination of kernel support functions (Vapnik’s support vector machines). Risk estimates for a misclassification as a function of the training sample size and other model parameters are obtained.