Approximation with neural networks activated by ramp sigmoids
type: Item
instance of: scholarly article
title: Approximation with neural networks activated by ramp sigmoids
zbMATH Open document ID: 1203.82069
author: Gerald H. L. Cheang
published in: Journal of Approximation Theory
publication date: 2010-09-29T00:00:00Z
review text: One proves that functions defined on a \(d\)-dimensional domain \(S\), with bounded variation with respect to a class of ellipsoids contained in \(S\) , can be accurately approximated by two-hidden-layer feedforward neural networks. The approximation error depends on the number of neurons in both hidden layers, space dimension and function variation. As activation function the rump sigmoid is considered. The author extensively refers to his earlier paper: \textit{G. H. L. Cheang} and \textit{A. R. Barron} in [J. Approximation Theory 104, No.~2, 183--203 (2000; Zbl 0991.41012)].
reviewed by: Claudia Simionescu-Badea
Mathematics Subject Classification ID: 82C32
Mathematics Subject Classification ID: 62M45
zbMATH DE Number: 5793307
zbMATH Keywords: two-hidden-layer neural net
zbMATH Keywords: ramp sigmoid
zbMATH Keywords: approximation error bounds
MaRDI profile type: Publication
full work available at URL: j.jat.2010.03.004
OpenAlex ID: W1968562765
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DOI: 10.1016/J.JAT.2010.03.004
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description: scientific article; zbMATH DE number 5793307