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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Official URL: http://dx.doi.org/10.1007/s10287-008-0072-5
Abstract
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: | 13 Sep 2013 09:25 |
| Last Modified: | 16 Sep 2013 12:03 |
| URI: | http://eprints.imtlucca.it/id/eprint/1708 |
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- The weight-decay technique in learning from data: an optimization point of view. (deposited 13 Sep 2013 09:25) [Currently Displayed]
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