Regularized Least Square Regression with Unbounded and Dependent Sampling

Abstract EN

This paper mainly focuses on the least square regression problem for the α-mixing and ϕ-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.