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

W Pan, L Xu, SM Zhou, Z Fan, Y Li, S Feng, Shang-ming Zhou Orcid Logo

Expert Systems with Applications, Volume: 37, Issue: 2, Pages: 1071 - 1074

Swansea University Author: Shang-ming Zhou Orcid Logo

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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 ben...

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Published in: Expert Systems with Applications
ISSN: 0957-4174
Published: 2010
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa10070
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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.
Keywords: Bayesian learning; Modular neural network; Information aggregation; Combination; Modularity
College: Swansea University Medical School
Issue: 2
Start Page: 1071
End Page: 1074