Regularized greedy algorithms for network training with data noise
type: Item
instance of: scholarly article
title: Regularized greedy algorithms for network training with data noise
zbMATH Open document ID: 1078.41022
published in: Computing
publication date: 2005-04-29T00:00:00Z
review text: The aim of this paper is to construct a modified greedy algorithm applicable to an ill-posed function approximation problem in presence of data noise. The authors provide a detailed convergence analysis of the algorithm and discuss the choice of the iteration parameters. Applications to neural networks are presented.
reviewed by: Mikhail Yu. Kokurin
Mathematics Subject Classification ID: 41A50
Mathematics Subject Classification ID: 41A25
Mathematics Subject Classification ID: 92B20
zbMATH DE Number: 2162512
zbMATH Keywords: Greedy approximation
zbMATH Keywords: data noise
zbMATH Keywords: neural networks
zbMATH Keywords: regularization
zbMATH Keywords: training
author: Martin Burger
author: Andreas Hofinger
MaRDI profile type: Publication
full work available at URL: s00607-004-0081-3
OpenAlex ID: W2031959925
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DOI: 10.1007/S00607-004-0081-3
description: scientific article; zbMATH DE number 2162512