Semi-supervised topo-Bayesian ARTMAP for noisy data
This paper presents a novel semi-supervised ART network that inherits the ability of noise insensitivity, topology learning, and incremental learning from the Bayesian ARTMAP. It is combined with a label prediction strategy based on a clustering technique to determine the neighboring neurons. The pr...
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my.um.eprints.211172019-05-03T07:55:18Z http://eprints.um.edu.my/21117/ Semi-supervised topo-Bayesian ARTMAP for noisy data Nooralishahi, Parham Loo, Chu Kiong Seera, Manjeevan QA75 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) This paper presents a novel semi-supervised ART network that inherits the ability of noise insensitivity, topology learning, and incremental learning from the Bayesian ARTMAP. It is combined with a label prediction strategy based on a clustering technique to determine the neighboring neurons. The procedure of updating Bayesian ARTMAP is modified to allow the network in altering the learning rate. This results in a classifier that works online and lifts several limitations of the original Bayesian ARTMAP. It processes arbitrarily scaled values even when their range is not entirely known in advance. The classifier has the capability to be employed in online learning applications, in which no prior-knowledge about the structure and distribution of data is available. Experimental results indicate good results, even with noisy data. Elsevier 2018 Article PeerReviewed Nooralishahi, Parham and Loo, Chu Kiong and Seera, Manjeevan (2018) Semi-supervised topo-Bayesian ARTMAP for noisy data. Applied Soft Computing, 62. pp. 134-147. ISSN 1568-4946 https://doi.org/10.1016/j.asoc.2017.10.011 doi:10.1016/j.asoc.2017.10.011 |
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QA75 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) Nooralishahi, Parham Loo, Chu Kiong Seera, Manjeevan Semi-supervised topo-Bayesian ARTMAP for noisy data |
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This paper presents a novel semi-supervised ART network that inherits the ability of noise insensitivity, topology learning, and incremental learning from the Bayesian ARTMAP. It is combined with a label prediction strategy based on a clustering technique to determine the neighboring neurons. The procedure of updating Bayesian ARTMAP is modified to allow the network in altering the learning rate. This results in a classifier that works online and lifts several limitations of the original Bayesian ARTMAP. It processes arbitrarily scaled values even when their range is not entirely known in advance. The classifier has the capability to be employed in online learning applications, in which no prior-knowledge about the structure and distribution of data is available. Experimental results indicate good results, even with noisy data. |
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Article |
author |
Nooralishahi, Parham Loo, Chu Kiong Seera, Manjeevan |
author_facet |
Nooralishahi, Parham Loo, Chu Kiong Seera, Manjeevan |
author_sort |
Nooralishahi, Parham |
title |
Semi-supervised topo-Bayesian ARTMAP for noisy data |
title_short |
Semi-supervised topo-Bayesian ARTMAP for noisy data |
title_full |
Semi-supervised topo-Bayesian ARTMAP for noisy data |
title_fullStr |
Semi-supervised topo-Bayesian ARTMAP for noisy data |
title_full_unstemmed |
Semi-supervised topo-Bayesian ARTMAP for noisy data |
title_sort |
semi-supervised topo-bayesian artmap for noisy data |
publisher |
Elsevier |
publishDate |
2018 |
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http://eprints.um.edu.my/21117/ https://doi.org/10.1016/j.asoc.2017.10.011 |
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