A novel Bayesian learning method for information aggregation in modular neural networks

Pan Wang; Lida Xu; Shang-Ming Zhou; Zhun Fan; Youfeng Li; Shan Feng

doi:10.1016/j.eswa.2009.06.104

A novel Bayesian learning method for information aggregation in modular neural networks

Pan Wang, Lida Xu, Shang-Ming Zhou, Zhun Fan, Youfeng Li, Shan Feng

Research output: Contribution to journal › Journal article › Research › peer-review

Abstract

Modular neural network is a popular neural network model which has many successful applications. In this paper, a sequential Bayesian learning (SBL) is proposed for modular neural networks aiming at efficiently aggregating the outputs of members of the ensemble. The experimental results on eight benchmark problems have demonstrated that the proposed method can perform information aggregation efficiently in data modeling.

Original language	English
Journal	Expert Systems with Applications
Volume	37
Issue number	2
Pages (from-to)	1071-1074
ISSN	0957-4174
DOIs	https://doi.org/10.1016/j.eswa.2009.06.104
Publication status	Published - 2010

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title = "A novel Bayesian learning method for information aggregation in modular neural networks",

abstract = "Modular neural network is a popular neural network model which has many successful applications. In this paper, a sequential Bayesian learning (SBL) is proposed for modular neural networks aiming at efficiently aggregating the outputs of members of the ensemble. The experimental results on eight benchmark problems have demonstrated that the proposed method can perform information aggregation efficiently in data modeling.",

author = "Pan Wang and Lida Xu and Shang-Ming Zhou and Zhun Fan and Youfeng Li and Shan Feng",

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AU - Xu, Lida

AU - Zhou, Shang-Ming

AU - Fan, Zhun

AU - Li, Youfeng

AU - Feng, Shan

PY - 2010

Y1 - 2010

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AB - Modular neural network is a popular neural network model which has many successful applications. In this paper, a sequential Bayesian learning (SBL) is proposed for modular neural networks aiming at efficiently aggregating the outputs of members of the ensemble. The experimental results on eight benchmark problems have demonstrated that the proposed method can perform information aggregation efficiently in data modeling.

U2 - 10.1016/j.eswa.2009.06.104

DO - 10.1016/j.eswa.2009.06.104

M3 - Journal article

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VL - 37

SP - 1071

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JO - Expert Systems with Applications

JF - Expert Systems with Applications

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A novel Bayesian learning method for information aggregation in modular neural networks

Abstract

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