Publication Type
Journal Article
Version
publishedVersion
Publication Date
5-2004
Abstract
This paper introduces the Adaptive Resonance Theory under Constraint (ART-C 2A) learning paradigm based on ART 2A, which is capable of generating a user-defined number of recognition nodes through online estimation of an appropriate vigilance threshold. Empirical experiments compare the cluster validity and the learning efficiency of ART-C 2A with those of ART 2A, as well as three closely related clustering methods, namely online K-Means, batch K-Means, and SOM, in a quantitative manner. Besides retaining the online cluster creation capability of ART 2A, ART-C 2A gives the alternative clustering solution, which allows a direct control on the number of output clusters generated by the self-organizing process.
Keywords
Adaptive Resonance Theory (ART), clustering, constraint learning, neural networks
Discipline
Computer Engineering | Databases and Information Systems | OS and Networks
Research Areas
Data Science and Engineering
Publication
IEEE Transactions on Neural Networks
Volume
15
Issue
3
First Page
728
Last Page
737
ISSN
1045-9227
Identifier
10.1109/TNN.2004.826220
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
HE, Ji; TAN, Ah-hwee; and TAN, Chew-Lim.
Modified ART 2A growing network capable of generating a fixed number of nodes. (2004). IEEE Transactions on Neural Networks. 15, (3), 728-737.
Available at: https://ink.library.smu.edu.sg/sis_research/5238
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TNN.2004.826220
Included in
Computer Engineering Commons, Databases and Information Systems Commons, OS and Networks Commons