Approximating Multivariable Functions by Feedforward Neural Nets View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2013

AUTHORS

Paul C. Kainen , Věra Kůrková , Marcello Sanguineti

ABSTRACT

Theoretical results on approximation of multivariable functions by feedforward neural networks are surveyed. Some proofs of universal approximation capabilities of networks with perceptrons and radial units are sketched. Major tools for estimation of rates of decrease of approximation errors with increasing model complexity are proven. Properties of best approximation are discussed. Recent results on dependence of model complexity on input dimension are presented and some cases when multivariable functions can be tractably approximated are described More... »

PAGES

143-181

Book

TITLE

Handbook on Neural Information Processing

ISBN

978-3-642-36656-7
978-3-642-36657-4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-36657-4_5

DOI

http://dx.doi.org/10.1007/978-3-642-36657-4_5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1022857946


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