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.
|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|
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The weight-decay technique in learning from data: an optimization point of view. (deposited 13 Sep 2013 09:25)
- The Weight-Decay Technique in Learning from Data: An Optimization Point of View. (deposited 17 Sep 2013 13:11) [Currently Displayed]
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