【摘要】 研究l~P-系数正则化意义下Shannon采样学习算法的收敛速度估计问题.借助l~P-空间的凸性不等式给出了样本误差和正则化误差的上界估计,并给出了用K-泛函表示的逼近误差估计.将K-泛函的收敛速度估计转化为平移网络逼近问题,在此基础上给出了用概率表示的学习速度.更多还原

【Abstract】 In the present paper,we provide an investigation on the learning rate of the Shannon sampling algorithms with l~p-coefficient regularization.We give the upper bounds for the sample error and the regularization error with the convex inequality in l~p-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.更多还原