Regularized greedy algorithms for network training with data noise

DOI10.1007/s00607-004-0081-3zbMath1078.41022OpenAlexW2031959925MaRDI QIDQ1774153

Andreas Hofinger, Martin Burger

Publication date: 29 April 2005

Published in: Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00607-004-0081-3

zbMATH Keywords

regularizationneural networkstrainingGreedy approximationdata noise

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Best approximation, Chebyshev systems (41A50) Rate of convergence, degree of approximation (41A25)

Related Items (3)

Nonlinear function approximation: computing smooth solutions with an adaptive greedy algorithm ⋮ Regularized greedy algorithms for network training with data noise ⋮ Learning a function from noisy samples at a finite sparse set of points

Cites Work

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• A simple lemma on greedy approximation in Hilbert space and convergence rates for projection pursuit regression and neural network training
• Multilayer feedforward networks are universal approximators
• Regularized greedy algorithms for network training with data noise
• Nonlinear black-box modeling in system identification: A unified overview
• A note on error bounds for approximation in inner product spaces
• Universal approximation bounds for superpositions of a sigmoidal function
• Regularized data-driven construction of fuzzy controllers
• Training neural networks with noisy data as an ill-posed problem

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