Learning Rate of Least Square Regressions with Some Kind of Mercer Kernel

Abstract

We consider the error estimate of least square regression with data dependent hypothesis and coefficient regularization algorithms based on general kernel. When the kernel belongs to some kind of Mercer kernel, under a mild regularity condition on the regression function, we derive a dimensional-free learning rate m-1/6.