Model selection in function approximation problem based on statistical learning theory

In order to approximate multidimensional function it is necessary to select the complexity of the model used for approximation of unknown function. Method on basis of statistical learning theory for complexity estimation of a model is elaborated. Proposed method is significantly less computationally intensive compared to such classical methods as AIC or cross-validation