Least Square Regressions with Coefficient Regularization

We consider the least square regression with data dependent hypothesis and coefficient regularization algorithms based on general kernel. An explicit expression of the solution of this kernel scheme is derived. Then we provide a sample error with a decay of O(1/√m) and estimate the approximation error in terms of some kind of K-functional.