An Algorithm for Approximating Continuous Functions on Compact Subsets with a Neural Network with one Hidden Layer
Year: 2019
Type: preprint
Abstract: George Cybenko's landmark 1989 paper showed that there exists a feedforward neural network, with exactly one hidden layer (and a finite number of neurons), that can arbitrarily approximate a given continuous function $f$ on the unit hypercube. The paper did not address how to find the weight/parameters of such a network, or if finding them would be computationally feasible. This paper outlines an algorithm for a neural network with exactly one hidden layer to reconstruct any continuous scalar or vector valued continuous function.
Source: arXiv (Cornell University)
Author: Zaresky-Williams, Elliott
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2912908355