Learning performance of tikhonov regularization algorithm with strongly mixing samples

Abstract

The generalization performance is the main purpose of machine learning theoretical research. The previous bounds describing the generalization ability of Tikhonov regularization algorithm are almost all based on independent and identically distributed (i.i.d.) samples. In this paper we go far beyond this classical framework by establishing the bound on the generalization ability of Tikhonov regularization algorithm with exponentially strongly mixing observations. We then show that Tikhonov regularization algorithm with exponentially strongly mixing observations is consistent. © 2009 Springer Berlin Heidelberg.