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Gaussian kernel regularization is widely used in the machine learning literature and has proved successful in many empirical experiments. The periodic version of Gaussian kernel regularization has been shown to be minimax rate optimal in estimating functions in any finite order Sobolev space. However, for a data set with n points, the computation complexity of the Gaussian kernel regularization method is of order O(n 3 ). In this paper we propose to use binning to reduce the computation ofdoi:10.21236/ada473041 fatcat:vxdqvazc7jhwrizk26zhh2aire