Gnecco, Giorgio and Sanguineti, Marcello Regularization and Suboptimal Solutions in Learning from Data. In: Innovations in Neural Information Paradigms and Applications. Studies in Computational Intelligence (247). Springer, pp. 113-154. ISBN 978-3-642-04002-3 (2009)
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Abstract
Supervised learning from data is investigated from an optimization viewpoint. Ill-posedness issues of the learning problem are discussed and its Tikhonov, Ivanov, Phillips, and Miller regularizations are analyzed. Theoretical features of the optimization problems associated with these regularization techniques and their use in learning tasks are considered. Weight-decay learning is investigated, too. Exploiting properties of the functionals to be minimized in the various regularized problems, estimates are derived on the accuracy of suboptimal solutions formed by linear combinations of n-tuples of computational units, for values of n smaller than the number of data.
| Item Type: | Book Section |
|---|---|
| Identification Number: | https://doi.org/10.1007/978-3-642-04003-0_6 |
| Uncontrolled Keywords: | regularization techniques; accuracy of suboptimal solutions; ill-posedness; inverse problems; weight decay |
| 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 13:10 |
| Last Modified: | 17 Sep 2013 13:10 |
| URI: | http://eprints.imtlucca.it/id/eprint/1776 |
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Regularization and Suboptimal Solutions in Learning from Data. (deposited 12 Sep 2013 10:56)
- Regularization and Suboptimal Solutions in Learning from Data. (deposited 17 Sep 2013 13:10) [Currently Displayed]
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