Regularized least square regression with unbounded and dependent sampling (Q369717)

Summary: This paper mainly focuses on the least square regression problem for \(\alpha\)-mixing and \(\phi\)-mixing processes. The standard bound assumption for output data is abandoned and the learning algorithm is implemented with samples drawn from a dependent sampling process with a more general output data condition. Capacity independent error bounds and learning rates are deduced by means of the integral operator technique.