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The new ERA in supervised learning

Gorse, D; Shepherd, AJ; Taylor, JG; (1997) The new ERA in supervised learning. NEURAL NETWORKS , 10 (2) 343 - 352.

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Abstract

Conventional methods of supervised learning are inevitably faced with the problem of local minima; evidence is presented that second order methods such as the conjugate gradient and quasi-Newton techniques are particularly susceptible to being trapped in sub-optimal solutions. A new technique, expanded range approximation (ERA), is presented, which by the use of a homotopy on the range of the target outputs allows supervised learning methods to find a global minimum of the error function in almost every case. (C) 1997 Elsevier Science Ltd All Rights Reserved.

Type: Article
Title: The new ERA in supervised learning
Keywords: global optimisation, local minima, homotopy, range expansion, TUNNELING ALGORITHM, LOCAL MINIMA, OPTIMIZATION, PROPAGATION, PERCEPTRONS
UCL classification: UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/79386
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