Decomposition Techniques for Multilayer Perceptron Training

In this paper, we consider the learning problem of multilayer perceptrons (MLPs) formulated as the problem of minimizing a smooth error function. As well known, the learning problem of MLPs can be a difficult nonlinear nonconvex optimization problem. Typical difficulties can be the presence of exten...

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 27; číslo 11; s. 2146 - 2159
Hlavní autori:	Grippo, Luigi, Manno, Andrea, Sciandrone, Marco
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.11.2016
Predmet:	Approximation algorithms Convergence Decomposition techniques extreme learning machine (ELM) feedforward neural networks Indexes Linear programming Minimization multilayer perceptrons (MLPs) Optimization optimization methods for supervised learning Training
ISSN:	2162-237X, 2162-2388, 2162-2388
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