%0 Journal Article
%@ 1619-697X
%A Gnecco, Giorgio
%A Sanguineti, Marcello
%D 2009
%F eprints:1708
%I Springer
%J Computational Management Science
%K Learning from data; Regularization; Weight decay; Suboptimal solutions; Rates of convergence
%N 1
%P 53-79
%T The weight-decay technique in learning from data: an optimization point of view
%U http://eprints.imtlucca.it/1708/
%V 6
%X 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.