Error analysis of kernel regularized pairwise learning with a strongly convex loss

Abstract

This paper presents a detailed performance analysis for the kernel-based regularized pairwise learning model associated with a strongly convex loss. The robustness for the model is analyzed by applying an improved convex analysis method. The results show that the regularized pairwise learning model has better qualitatively robustness according to the probability measure. Some new comparison inequalities are provided, with which the convergence rates are derived. In particular an explicit learning rate is obtained in case that the loss is the least square loss.