Kernel Bayesian ART and ARTMAP
Adaptive Resonance Theory (ART) is one of the successful approaches to resolving “the plasticity–stability dilemma” in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state...
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| Format: | Article |
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Elsevier
2018
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| _version_ | 1825721666145091584 |
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| author | Masuyama, Naoki Loo, Chu Kiong Dawood, Farhan |
| author_facet | Masuyama, Naoki Loo, Chu Kiong Dawood, Farhan |
| author_sort | Masuyama, Naoki |
| collection | UM |
| description | Adaptive Resonance Theory (ART) is one of the successful approaches to resolving “the plasticity–stability dilemma” in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable. This paper introduces Kernel Bayesian ART (KBA) and ARTMAP (KBAM) by integrating Kernel Bayes’ Rule (KBR) and Correntropy Induced Metric (CIM) to Bayesian ART (BA) and ARTMAP (BAM), respectively, while maintaining the properties of BA and BAM. The kernel frameworks in KBA and KBAM are able to avoid the curse of dimensionality. In addition, the covariance-free Bayesian computation by KBR provides the efficient and stable computational capability to KBA and KBAM. Furthermore, Correntropy-based similarity measurement allows improving the noise reduction ability even in the high dimensional space. The simulation experiments show that KBA performs an outstanding self-organizing capability than BA, and KBAM provides the superior classification ability than BAM, respectively. |
| first_indexed | 2024-03-06T05:53:49Z |
| format | Article |
| id | um.eprints-21323 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:53:49Z |
| publishDate | 2018 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | um.eprints-213232019-05-27T05:04:14Z http://eprints.um.edu.my/21323/ Kernel Bayesian ART and ARTMAP Masuyama, Naoki Loo, Chu Kiong Dawood, Farhan QA75 Electronic computers. Computer science Adaptive Resonance Theory (ART) is one of the successful approaches to resolving “the plasticity–stability dilemma” in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable. This paper introduces Kernel Bayesian ART (KBA) and ARTMAP (KBAM) by integrating Kernel Bayes’ Rule (KBR) and Correntropy Induced Metric (CIM) to Bayesian ART (BA) and ARTMAP (BAM), respectively, while maintaining the properties of BA and BAM. The kernel frameworks in KBA and KBAM are able to avoid the curse of dimensionality. In addition, the covariance-free Bayesian computation by KBR provides the efficient and stable computational capability to KBA and KBAM. Furthermore, Correntropy-based similarity measurement allows improving the noise reduction ability even in the high dimensional space. The simulation experiments show that KBA performs an outstanding self-organizing capability than BA, and KBAM provides the superior classification ability than BAM, respectively. Elsevier 2018 Article PeerReviewed Masuyama, Naoki and Loo, Chu Kiong and Dawood, Farhan (2018) Kernel Bayesian ART and ARTMAP. Neural Networks, 98. pp. 76-86. ISSN 0893-6080, DOI https://doi.org/10.1016/j.neunet.2017.11.003 <https://doi.org/10.1016/j.neunet.2017.11.003>. https://doi.org/10.1016/j.neunet.2017.11.003 doi:10.1016/j.neunet.2017.11.003 |
| spellingShingle | QA75 Electronic computers. Computer science Masuyama, Naoki Loo, Chu Kiong Dawood, Farhan Kernel Bayesian ART and ARTMAP |
| title | Kernel Bayesian ART and ARTMAP |
| title_full | Kernel Bayesian ART and ARTMAP |
| title_fullStr | Kernel Bayesian ART and ARTMAP |
| title_full_unstemmed | Kernel Bayesian ART and ARTMAP |
| title_short | Kernel Bayesian ART and ARTMAP |
| title_sort | kernel bayesian art and artmap |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT masuyamanaoki kernelbayesianartandartmap AT loochukiong kernelbayesianartandartmap AT dawoodfarhan kernelbayesianartandartmap |