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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Bibliographic Details

Published in:	IEEE transaction on neural networks and learning systems Vol. 27; no. 11; pp. 2146 - 2159
Main Authors:	Grippo, Luigi, Manno, Andrea, Sciandrone, Marco
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.11.2016
Subjects:	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
Online Access:	Get full text
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