Journal article 22416 views
A novel Bayesian learning method for information aggregation in modular neural networks
Expert Systems with Applications, Volume: 37, Issue: 2, Pages: 1071 - 1074
Swansea University Author:
Shang-ming Zhou
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DOI (Published version): 10.1016/j.eswa.2009.06.104
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...
| Published in: | Expert Systems with Applications |
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| ISSN: | 0957-4174 |
| Published: |
2010
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| Online Access: |
Check full text
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| 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. |
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| Keywords: |
Bayesian learning; Modular neural network; Information aggregation; Combination; Modularity |
| College: |
Faculty of Medicine, Health and Life Sciences |
| Issue: |
2 |
| Start Page: |
1071 |
| End Page: |
1074 |