Model Complexity of Neural Networks in High-Dimensional Approximation

摘要

The role of dimensionality in approximation by neural networks is investigated. Methods from nonlinear approximation theory are used to describe sets of functions which can be approximated by neural networks with a polynomial dependence of model complexity on the input dimension. The results are illustrated by examples of Gaussian radial networks.