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Sobolev Error Estimates and a Bernstein Inequality for Scattered Data Interpolation via Radial Basis Functions

机译:径向基函数的分散数据插值的Sobolev误差估计和Bernstein不等式

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Error estimates for scattered-data interpolation via radial basis functions (RBFs) for target functions in the associated reproducing kernel Hilbert space (RKHS) have been known for a long time. Recently, these estimates have been extended to apply to certain classes of target functions generating the data which are outside the associated RKHS. However, these classes of functions still were not "large" enough to be applicable to a number of practical situations. In this paper we obtain Sobolev-type error estimates on compact regions of Rn when the RBFs have Fourier transforms that decay algebraically. In addition, we derive a Bernstein inequality for spaces of finite shifts of an RBF in terms of the minimal separation parameter.
机译:很长时间以来,已经知道了通过径向基函数(RBF)对相关再现内核希尔伯特空间(RKHS)中的目标函数进行散乱数据插值的误差估计。最近,这些估计已扩展到适用于某些类别的目标函数,这些目标函数会生成关联RKHS之外的数据。但是,这些功能类别仍然不够“庞大”,无法应用于许多实际情况。在本文中,当RBF具有经代数衰减的傅里叶变换时,我们可以在Rn的紧致区域上获得Sobolev型误差估计。此外,根据最小分离参数,我们得出了RBF有限位移空间的伯恩斯坦不等式。

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