Regularization and Suboptimal Solutions in Learning from Data

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:	12 Sep 2013 10:56
Last Modified:	16 Sep 2013 12:03
URI:	http://eprints.imtlucca.it/id/eprint/1695

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• Regularization and Suboptimal Solutions in Learning from Data. (deposited 12 Sep 2013 10:56) [Currently Displayed]
  • Regularization and Suboptimal Solutions in Learning from Data. (deposited 17 Sep 2013 13:10)

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