Gnecco, Giorgio and Sanguineti, Marcello Regularization Techniques and Suboptimal Solutions to Optimization Problems in Learning from Data. Neural Computation, 22 (3). pp. 793-829. ISSN 0899-7667 (2010)
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Official URL: http://dx.doi.org/10.1162/neco.2009.05-08-786
Abstract
Various regularization techniques are investigated in supervised learning from data. Theoretical features of the associated optimization problems are studied, and sparse suboptimal solutions are searched for. Rates of approximate optimization are estimated for sequences of suboptimal solutions formed by linear combinations of n-tuples of computational units, and statistical learning bounds are derived. As hypothesis sets, reproducing kernel Hilbert spaces and their subsets are considered.
| Item Type: | Article |
|---|---|
| Identification Number: | https://doi.org/10.1162/neco.2009.05-08-786 |
| 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 10:17 |
| Last Modified: | 16 Sep 2013 12:03 |
| URI: | http://eprints.imtlucca.it/id/eprint/1713 |
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