relation: http://eprints.imtlucca.it/1713/
title: Regularization Techniques and Suboptimal Solutions to Optimization Problems in Learning from Data
creator: Gnecco, Giorgio
creator: Sanguineti, Marcello
subject: QA75 Electronic computers. Computer science
description: 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.
publisher: MIT Press
date: 2010
type: Article
type: PeerReviewed
identifier:   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)  
relation: http://dx.doi.org/10.1162/neco.2009.05-08-786
relation: 10.1162/neco.2009.05-08-786