Fast Rates for Regularized Least-squares Algorithm
Author(s)Caponnetto, Andrea; Vito, Ernesto De
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.
Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
AI, optimal rates, regularized least-squares, reproducing kernel Hilbert space, effe