Regularization techniques and suboptimal solutions to optimization problems in learning from data

Neural Computation
Giorgio Gnecco, Marcello Sanguineti

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.

References

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Citations

Jan 24, 2013·Neural Computation·Giorgio GneccoMarcello Sanguineti
Jun 28, 2011·Neural Networks : the Official Journal of the International Neural Network Society·Giorgio GneccoMarcello Sanguineti
Dec 23, 2014·IEEE Transactions on Neural Networks and Learning Systems·Jan Chorowski, Jacek M Zurada
Sep 8, 2012·Neural Networks : the Official Journal of the International Neural Network Society·Zhiquan QiYong Shi
May 18, 2016·Neural Networks : the Official Journal of the International Neural Network Society·Simone ScardapaneAurelio Uncini
Jul 28, 2021·Neural Networks : the Official Journal of the International Neural Network Society·Buse Melis Ozyildirim, Mariam Kiran

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