Regularized Least Square Regression with Unbounded and Dependent Sampling

Abstract

This paper mainly focuses on the least square regression problem for the $\alpha$-mixing and $\varphi$-mixing processes. The standard bound assumption for output data is abandoned and the learning algorithm is implemented with samples drawn from 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.