RBF网络的L/sub 1/收敛速率和核回归估计的一些结果

A. Krzyżak, L. Xu
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引用次数: 0

摘要

本文不是研究Xu-Krzyzak-Yuille(1992 & 1993)给出的核回归估计器(KRE)和径向基函数(RBF)网络的L/sub /收敛率,而是研究KRE和RBF网络的平均积分绝对误差(MIAE)的收敛性质。结果表明,当网络的规模和训练序列的规模趋近于/spl infin/时,KRE和RBF网络的MIAE收敛于零,逼近Lipschitz函数时,MIAE收敛速率的上界为O(n-/sup /spl alpha/s/sub (2+s)/(/sub 2//spl alpha/+d)/)
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Some results on L/sub 1/ convergence rate of RBF networks and kernel regression estimators
Rather than studying the L/sub 2/ convergence rates of kernel regression estimators (KRE) and radial basis function (RBF) nets given in Xu-Krzyzak-Yuille (1992 & 1993), we study convergence properties of the mean integrated absolute error (MIAE) for KRE and RBF nets. It has been shown that MIAE of KRE and RBF nets can converge to zero as the size of networks and the size of the training sequence tend to /spl infin/, and that the upper bound for the convergence rate of MIAE is O(n-/sup /spl alpha/s/sub (2+s)/( /sub 2//spl alpha/+d)/) for approximating Lipschitz functions.<>
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