An Integral Upper Bound for Neural Network Approximation

Abstract

For functions with suitable integral representations in the form of networks with infinitely many hidden units, upper bounds are derived on the speed of decrease of approximation error as the number of network units increases. These bounds are obtained using the framework of Bochner.

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  • Článek v odborném periodiku