Replacing points by compacta in neural network approximation
نویسنده
چکیده
It is shown that cartesian product and pointwise-sum with a fixed compact set preserve various approximation-theoretic properties. Results for pointwise-sum are proved for F -spaces and so hold for any normed linear space, while the other results hold in general metric spaces. Applications are given to approximation of Lp-functions on the d-dimensional cube, 1 ≤ p < ∞, by linear combinations of halfspace characteristic functions; i.e., by Heaviside perceptron networks.
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