论文标题
基于最大似然估计的核插值的自适应残差子采样算法
An adaptive residual sub-sampling algorithm for kernel interpolation based on maximum likelihood estimations
论文作者
论文摘要
在本文中，我们提出了[9]中剩余子采样方法（RSM）的增强版本，以通过径向基础函数（RBFS）进行自适应插值。更确切地说，我们在子采样方法的背景下介绍了最佳选择RBF形状参数的最大概况可能性估计（MPLE）标准。此选择是完全自动的，为任何RBF提供了高度可靠，准确的结果，并且与原始RSM不同，可以保证RBF interpolant独特地存在。这种称为MPLE-RSM的新方法的功效通过在某些1D和2D基准目标函数上的数值实验测试。
In this paper we propose an enhanced version of the residual sub-sampling method (RSM) in [9] for adaptive interpolation by radial basis functions (RBFs). More precisely, we introduce in the context of sub-sampling methods a maximum profile likelihood estimation (MPLE) criterion for the optimal selection of the RBF shape parameter. This choice is completely automatic, provides highly reliable and accurate results for any RBFs, and, unlike the original RSM, guarantees that the RBF interpolant exists uniquely. The efficacy of this new method, called MPLE-RSM, is tested by numerical experiments on some 1D and 2D benchmark target functions.
