Neural networks using Bayesian training

Gabriela Andrejková; Miroslav Levický

Kybernetika (2003)

  • Volume: 39, Issue: 5, page [511]-520
  • ISSN: 0023-5954

Abstract

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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.

How to cite

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Andrejková, Gabriela, and Levický, Miroslav. "Neural networks using Bayesian training." Kybernetika 39.5 (2003): [511]-520. <http://eudml.org/doc/33662>.

@article{Andrejková2003,
abstract = {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.},
author = {Andrejková, Gabriela, Levický, Miroslav},
journal = {Kybernetika},
keywords = {neural network; Bayesian probability theory; geomagnetic storm; prediction; geomagnetic storm; prediction},
language = {eng},
number = {5},
pages = {[511]-520},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Neural networks using Bayesian training},
url = {http://eudml.org/doc/33662},
volume = {39},
year = {2003},
}

TY - JOUR
AU - Andrejková, Gabriela
AU - Levický, Miroslav
TI - Neural networks using Bayesian training
JO - Kybernetika
PY - 2003
PB - Institute of Information Theory and Automation AS CR
VL - 39
IS - 5
SP - [511]
EP - 520
AB - 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.
LA - eng
KW - neural network; Bayesian probability theory; geomagnetic storm; prediction; geomagnetic storm; prediction
UR - http://eudml.org/doc/33662
ER -

References

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  11. MacKay D. J. C., Bayesian Methods for Neural Networks: Theory and Applications, Neural Network Summer School, 1995 
  12. MacKay D. J. C., 10.1162/neco.1992.4.3.448, Neural Computation 4, pp. 448–472 DOI10.1162/neco.1992.4.3.448
  13. Müller P., Insua D. R., 10.1162/089976698300017737, Neural Computation 10, pp. 749–770 DOI10.1162/089976698300017737
  14. Neal R. M., Probabilistic Inference Using Markov Chain Monte Carlo Methods, Technical Report CRG-TR-93-1, University of Toronto, 1993 
  15. Neal R. M., Bayesian Training of Backpropagation Networks by the Hybrid Monte Carlo Method, Technical Report CRG-TR-92-1, University of Toronto, 1992 
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