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Overtraining, Regul...
Overtraining, Regularization and Searching for Minimum in Neural Networks
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- Ljung, Lennart, 1946- (författare)
- Linköpings universitet,Reglerteknik,Tekniska högskolan
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- Sjöberg, Jonas (författare)
- Linköpings universitet,Reglerteknik,Tekniska högskolan
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(creator_code:org_t)
- 1992
- 1992
- Engelska.
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Ingår i: 4th IFAC Symposium on Adaptive Systems in Control and Signal Processing. - 9780080425962 ; , s. 669-674
- Relaterad länk:
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http://urn.kb.se/res...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Nyckelord
- Neural network models
- Dynamical systems
- Parameters
- Variance error
- Regularization
- Automatic control
- Reglerteknik
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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