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. 113154.
ISBN 9783642040023
(2009)
Full text not available from this repository.
Abstract
Supervised learning from data is investigated from an optimization viewpoint. Illposedness 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. Weightdecay 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 ntuples of computational units, for values of n smaller than the number of data.
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