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In this paper, we propose an efficient parameter tuning-free squared-loss mutual information (SMI) estimator in a form of a radial basis function (RBF) network. The input layer of the proposed network propagates a sample pair of two random variables to the hidden layer. The propagated samples are then transformed by a set of Gaussian RBF kernels with randomly determined kernel centers and widths similar...
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