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Overtraining, Regularization and Searching for Minimum in Neural Networks
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
1991 (English)Report (Other academic)
Abstract [en]

Neural network models for dynamical systems have been subject of considerable interest lately. They are often characterized by the fact that they use a fairly large amount of parameters. Here we address the problem why this can be done without the usual penalty in terms of a large variance error. We show that reguralization is a key explanation, and that terminating a gradient search ("backpropagation") before the true criterion minimum is found is a way of achieving regularization. This, among other things, also explains the concept of "overtraining" in neural nets.

Place, publisher, year, edition, pages
Linköping: Linköping University , 1991.
Series
LiTH-ISY-I, ISSN 8765-4321 ; 1297
Keyword [en]
Neural network models, Dynamical systems, Parameters, Variance error, Regularization
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-55483OAI: oai:DiVA.org:liu-55483DiVA: diva2:316120
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2013-07-29

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Ljung, Lennart

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  • de-DE
  • en-GB
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  • nn-NB
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  • Other locale
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Output format
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