Kernel Bayesian ART and ARTMAP

Masuyama, Naoki and Loo, Chu Kiong and Dawood, Farhan (2018) Kernel Bayesian ART and ARTMAP. Neural Networks, 98. pp. 76-86. ISSN 0893-6080

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Official URL: https://doi.org/10.1016/j.neunet.2017.11.003

Abstract

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.

Item Type: Article
Uncontrolled Keywords: Adaptive resonance theory; Bayesian ARTMAP; Classification; Kernel Bayes Rule; Correntropy induced metric
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 27 May 2019 05:04
Last Modified: 27 May 2019 05:04
URI: http://eprints.um.edu.my/id/eprint/21323

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