The Learning Rate of lp -coefficient Regularized Shannon Sampling Algorithm

Abstract

Abstract: In the present paper, we provide an investigation on the learning rate of the Shannon sampling algorithms with l-coefficient regularization. We give the upper bounds for the sample error and the regularization error with the convex inequality in l-spaces and show the approximation error by a K-functional whose convergence rate can be sum up to the translation network approximation. Basing on these estimates we show an explicit learning rate in possibility.