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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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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...

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Published in: Expert Systems with Applications
ISSN: 0957-4174
Published: 2010
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URI: https://cronfa.swan.ac.uk/Record/cronfa10070
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first_indexed 2013-07-23T12:02:44Z
last_indexed 2018-02-09T04:38:39Z
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spelling 2017-12-28T10:47:30.9107946 v2 10070 2012-03-21 A novel Bayesian learning method for information aggregation in modular neural networks 118578a62021ba8ef61398da0a8750da 0000-0002-0719-9353 Shang-ming Zhou Shang-ming Zhou true false 2012-03-21 BMS 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. Journal Article Expert Systems with Applications 37 2 1071 1074 0957-4174 Bayesian learning; Modular neural network; Information aggregation; Combination; Modularity 31 3 2010 2010-03-31 10.1016/j.eswa.2009.06.104 COLLEGE NANME Biomedical Sciences COLLEGE CODE BMS Swansea University 2017-12-28T10:47:30.9107946 2012-03-21T16:17:09.0000000 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine W Pan 1 L Xu 2 SM Zhou 3 Z Fan 4 Y Li 5 S Feng 6 Shang-ming Zhou 0000-0002-0719-9353 7
title A novel Bayesian learning method for information aggregation in modular neural networks
spellingShingle A novel Bayesian learning method for information aggregation in modular neural networks
Shang-ming Zhou
title_short A novel Bayesian learning method for information aggregation in modular neural networks
title_full A novel Bayesian learning method for information aggregation in modular neural networks
title_fullStr A novel Bayesian learning method for information aggregation in modular neural networks
title_full_unstemmed A novel Bayesian learning method for information aggregation in modular neural networks
title_sort A novel Bayesian learning method for information aggregation in modular neural networks
author_id_str_mv 118578a62021ba8ef61398da0a8750da
author_id_fullname_str_mv 118578a62021ba8ef61398da0a8750da_***_Shang-ming Zhou
author Shang-ming Zhou
author2 W Pan
L Xu
SM Zhou
Z Fan
Y Li
S Feng
Shang-ming Zhou
format Journal article
container_title Expert Systems with Applications
container_volume 37
container_issue 2
container_start_page 1071
publishDate 2010
institution Swansea University
issn 0957-4174
doi_str_mv 10.1016/j.eswa.2009.06.104
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
document_store_str 0
active_str 0
description 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.
published_date 2010-03-31T03:10:42Z
_version_ 1763749964928778240
score 11.012678

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