Coefficient Regularized Algorithms for Learning and Classification

We study the learning rate for the least square regression with data dependent hypothesis and coefficient regularization algorithms based on general kernel. We give some estimates for the learning raters of both regression and classification when the hypothesis spaces are sample dependent. Under a very mild condition on the kernels we provide learning error by using K-functional whose rates are estimated when the target functions are in the range of the Hilbert Schmidt integral operator.