在这篇论文,我们由复制核空格在上调查近似的顺序(1,1 ) 在加权的 L [p ] 空格。我们首先重申从复制内核空格的看法的翻译网络;然后在 Jacobi 直角的多项式,我们与建立一种杰克逊不平等描述错误估计的帮助下构造接近的操作员的一个序列。最后,结果被用来讨论从学习理论产生的一个近似问题。
In this paper, we investigate the order of approximation by reproducing kernel spaces on (-1, 1) in weighted L^p spaces. We first restate the translation network from the view of reproducing kernel spaces and then construct a sequence of approximating operators with the help of Jacobi orthogonal polynomials, with which we establish a kind of Jackson inequality to describe the error estimate. Finally, The results are used to discuss an approximation problem arising from learning theory.