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The Weight-Decay Technique in Learning from Data: An Optimization Point of View

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)

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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.

Item Type: Article
Identification Number: 10.1007/s10287-008-0072-5
Projects: Partially supported by a PRIN grant from the Italian Ministry for University and Research, project “Models and Algorithms for Robust Network Optimization”.
Uncontrolled Keywords: Learning from data; Regularization; Weight decay; Suboptimal solutions; Rates of convergence
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Research Area: Computer Science and Applications
Depositing User: Giorgio Gnecco
Date Deposited: 17 Sep 2013 13:11
Last Modified: 17 Sep 2013 13:11
URI: http://eprints.imtlucca.it/id/eprint/1796

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