relation: http://eprints.imtlucca.it/1708/
title: The weight-decay technique in learning from data: an optimization point of view
creator: Gnecco, Giorgio
creator: Sanguineti, Marcello
subject: QA75 Electronic computers. Computer science
description: The technique known as “weight decay” in the literature about learning from data is investigated using tools from regularization theory. Weight-decay regularization is compared with Tikhonov’s regularization of the learning problem and with a mixed regularized learning technique. The accuracies of suboptimal solutions to weight-decay learning are estimated for connectionistic models with a-priori fixed numbers of computational units.
publisher: Springer
date: 2009
type: Article
type: PeerReviewed
identifier:   Gnecco, Giorgio and Sanguineti, Marcello  The weight-decay technique in learning from data: an optimization point of view.  Computational Management Science, 6 (1).  pp. 53-79.  ISSN 1619-697X      (2009)  
relation: http://dx.doi.org/10.1007/s10287-008-0072-5
relation: 10.1007/s10287-008-0072-5