Bayesian Neural Networks and Its Application
The Bayesian approach provides consistent way to do inference by integrating the evidence from data with prior knowledge from the problem. Bayesian neural networks can overcome the main difficulty of controlling the modelpsilas complexity in modelling building of standard neural network. And the Bay...
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| creator | Chunling Fan Feng Gao Sitong Sun Fengying Cui |
| description | The Bayesian approach provides consistent way to do inference by integrating the evidence from data with prior knowledge from the problem. Bayesian neural networks can overcome the main difficulty of controlling the modelpsilas complexity in modelling building of standard neural network. And the Bayesian approach offers efficient tools for avoiding overfitting even with very complex models, and facilitates estimation of the confidence intervals of the results. In this paper, we review the Bayesian methods for neural networks. And then the structure of Bayesian neural networks is designed in this paper, and real detected drift data of a DTG is used to prove the effectiveness of the method. The results show the Bayesian neural networks methods possess better predictive precision. |
| doi_str_mv | 10.1109/ICNC.2008.624 |
| format | Conference Proceeding |
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Bayesian neural networks can overcome the main difficulty of controlling the modelpsilas complexity in modelling building of standard neural network. And the Bayesian approach offers efficient tools for avoiding overfitting even with very complex models, and facilitates estimation of the confidence intervals of the results. In this paper, we review the Bayesian methods for neural networks. And then the structure of Bayesian neural networks is designed in this paper, and real detected drift data of a DTG is used to prove the effectiveness of the method. 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Bayesian neural networks can overcome the main difficulty of controlling the modelpsilas complexity in modelling building of standard neural network. And the Bayesian approach offers efficient tools for avoiding overfitting even with very complex models, and facilitates estimation of the confidence intervals of the results. In this paper, we review the Bayesian methods for neural networks. And then the structure of Bayesian neural networks is designed in this paper, and real detected drift data of a DTG is used to prove the effectiveness of the method. 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Bayesian neural networks can overcome the main difficulty of controlling the modelpsilas complexity in modelling building of standard neural network. And the Bayesian approach offers efficient tools for avoiding overfitting even with very complex models, and facilitates estimation of the confidence intervals of the results. In this paper, we review the Bayesian methods for neural networks. And then the structure of Bayesian neural networks is designed in this paper, and real detected drift data of a DTG is used to prove the effectiveness of the method. The results show the Bayesian neural networks methods possess better predictive precision.</abstract><pub>IEEE</pub><doi>10.1109/ICNC.2008.624</doi><tpages>5</tpages></addata></record> |
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| ispartof | 2008 Fourth International Conference on Natural Computation, 2008, Vol.3, p.446-450 |
| issn | 2157-9555 |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Automatic control Automation Bayesian methods Computer networks Educational institutions Neural networks Predictive models Probability distribution Statistical distributions Sun |
| title | Bayesian Neural Networks and Its Application |
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