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Regularization tools for training large feed-forward neural networks using automatic differentiation
Umeå University, Faculty of Science and Technology, Departement of Computing Science.
Umeå University, Faculty of Science and Technology, Departement of Computing Science.
Umeå University, Faculty of Science and Technology, Departement of Computing Science.
Umeå University, Faculty of Science and Technology, Departement of Computing Science.
1998 (English)In: Optimization Methods & Software, Vol. 10, no 1, 49-69 p.Article in journal (Refereed) Published
Abstract [en]

We describe regularization tools for training large-scale artificial feed-forward neural networks. We propose algorithms that explicitly use a sequence of Tikhonov regularized nonlinear least squares problems. For large-scare problems, methods using new special purpose automatic differentiation are used in a conjugate gradient method for computing a truncated Gauss-Newton search direction. The algorithms developed utilize the structure of the problem in different ways and perform much better than a Polak-Ribiere based method. All algorithms are tested using benchmark problems and guidelines by Lutz Prechelt in the Probenl package. All software is written in Matlab and gathered in a toolbox.

Place, publisher, year, edition, pages
1998. Vol. 10, no 1, 49-69 p.
Identifiers
URN: urn:nbn:se:umu:diva-21927ISBN: 1055-6788 OAI: oai:DiVA.org:umu-21927DiVA: diva2:212182
Available from: 2009-04-21 Created: 2009-04-21 Last updated: 2009-04-21

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