On the universal approximation of real functions with varying domain
Abstract
We establish sufficient conditions for the density of shallow neural networks C89 on the family of continuous real functions defined on a compact metric space, taking into account variations in the function domains. For this we use the Gromov-Hausdorff distance defined in 5G.
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