Robust Formulations for Training Multilayer Perceptrons

DOI10.1162/089976604322860721zbMath1097.68594OpenAlexW2139341241WikidataQ110035735 ScholiaQ110035735MaRDI QIDQ4819818

Tommi Kärkkäinen, Erkki Heikkola

Publication date: 5 October 2004

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/089976604322860721

zbMATH Keywords

nonlinear regression approximation

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (10)

Comparing different nonsmooth minimization methods and software ⋮ Comparison of internal clustering validation indices for prototype-based clustering ⋮ Diagonal bundle method with convex and concave updates for large-scale nonconvex and nonsmooth optimization ⋮ Unnamed Item ⋮ Diagonal bundle method for nonsmooth sparse optimization ⋮ A feasible point method with bundle modification for nonsmooth convex constrained optimization ⋮ Denoising of smooth images using \(L^{1}\)-fitting ⋮ Diagonal discrete gradient bundle method for derivative free nonsmooth optimization ⋮ Subgradient and Bundle Methods for Nonsmooth Optimization ⋮ Limited memory discrete gradient bundle method for nonsmooth derivative-free optimization

Cites Work

• Uniqueness of the spatial median
• Comparison of formulations and solution methods for image restoration problems
• The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network
• MLP in Layer-Wise Form with Applications to Weight Decay
• Affine Invariant Multivariate Sign and Rank Tests and Corresponding Estimates: a Review

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