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Article

Title: Neural networks using Bayesian training (English)
Author: Andrejková, Gabriela
Author: Levický, Miroslav
Language: English
Journal: Kybernetika
ISSN: 0023-5954
Volume: 39
Issue: 5
Year: 2003
Pages: [511]-520
Summary lang: English
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Category: math
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Summary: Bayesian probability theory provides a framework for data modeling. In this framework it is possible to find models that are well-matched to the data, and to use these models to make nearly optimal predictions. In connection to neural networks and especially to neural network learning, the theory is interpreted as an inference of the most probable parameters for the model and the given training data. This article describes an application of Neural Networks using the Bayesian training to the problem of Predictions of Geomagnetic Storms. (English)
Keyword: neural network
Keyword: Bayesian probability theory
Keyword: geomagnetic storm
Keyword: prediction
MSC: 62F15
MSC: 62M45
MSC: 86A25
MSC: 86A32
idZBL: Zbl 1248.62174
idMR: MR2042338
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Date available: 2009-09-24T19:56:22Z
Last updated: 2015-03-24
Stable URL: http://hdl.handle.net/10338.dmlcz/135552
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