Modified ART 2A growing network capable of generating a fixed number of nodes
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 vali...
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2004
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sg-smu-ink.sis_research-62412020-07-23T18:25:24Z Modified ART 2A growing network capable of generating a fixed number of nodes HE, Ji TAN, Ah-hwee TAN, Chew-Lim 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. 2004-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5238 info:doi/10.1109/TNN.2004.826220 https://ink.library.smu.edu.sg/context/sis_research/article/6241/viewcontent/ARTC_TNN04.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Adaptive Resonance Theory (ART) clustering constraint learning neural networks Computer Engineering Databases and Information Systems OS and Networks |
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Adaptive Resonance Theory (ART) clustering constraint learning neural networks Computer Engineering Databases and Information Systems OS and Networks |
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Adaptive Resonance Theory (ART) clustering constraint learning neural networks Computer Engineering Databases and Information Systems OS and Networks HE, Ji TAN, Ah-hwee TAN, Chew-Lim Modified ART 2A growing network capable of generating a fixed number of nodes |
| description |
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. |
| format |
text |
| author |
HE, Ji TAN, Ah-hwee TAN, Chew-Lim |
| author_facet |
HE, Ji TAN, Ah-hwee TAN, Chew-Lim |
| author_sort |
HE, Ji |
| title |
Modified ART 2A growing network capable of generating a fixed number of nodes |
| title_short |
Modified ART 2A growing network capable of generating a fixed number of nodes |
| title_full |
Modified ART 2A growing network capable of generating a fixed number of nodes |
| title_fullStr |
Modified ART 2A growing network capable of generating a fixed number of nodes |
| title_full_unstemmed |
Modified ART 2A growing network capable of generating a fixed number of nodes |
| title_sort |
modified art 2a growing network capable of generating a fixed number of nodes |
| publisher |
Institutional Knowledge at Singapore Management University |
| publishDate |
2004 |
| url |
https://ink.library.smu.edu.sg/sis_research/5238 https://ink.library.smu.edu.sg/context/sis_research/article/6241/viewcontent/ARTC_TNN04.pdf |
| _version_ |
1770575345508941824 |