Fast Rates for Regularized Least-Squares Algorithm

摘要

We develop a theoretical analysis of generalization performances of regularized least-squares on reproducing kernel Hilbert spaces for supervised learning. we show that the concept of effective dimension of an integral operator plays a central role in the definition of a criterion for the choice of the regularization parameter as a function of the number of samples. In fact a minimax analysis is performed which shows asymptotic optimality of the above mentioned criterion.