Structural risk minimization algorithms for design of a linear regression

Finite optimal algorithms are proposed for restoration of a multi-dimensional regression in two classes of functions whose parameters are nonlinear. For classes of functions an ordered structure of embedded subclasses of functions is specified which influences the guaranteed estimates of the mean risk functional for samples of a fixed size.