relation: http://eprints.imtlucca.it/1669/
title: Some Comparisons of Model Complexity in Linear and Neural-Network Approximation
creator: Gnecco, Giorgio
creator: Kůrková, Věra
creator: Sanguineti, Marcello
subject: QA75 Electronic computers. Computer science
description: Capabilities of linear and neural-network models are compared from the point of view of requirements on the growth of model complexity with an increasing accuracy of approximation. Upper bounds on worst-case errors in approximation by neural networks are compared with lower bounds on these errors in linear approximation. The bounds are formulated in terms of singular numbers of certain operators induced by computational units and high-dimensional volumes of the domains of the functions to be approximated.
publisher: Springer
date: 2010
type: Book Section
type: PeerReviewed
identifier:   Gnecco, Giorgio and Kůrková, Věra and Sanguineti, Marcello  Some Comparisons of Model Complexity in Linear and Neural-Network Approximation.   In:  Artificial Neural Networks – ICANN 2010.   Lecture Notes in Computer Science  (6354).  Springer, pp. 358-367.  ISBN 978-3-642-15825-4      (2010)  
relation: http://dx.doi.org/10.1007/978-3-642-15825-4_48