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Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2004.826220
DC FieldValue
dc.titleModified ART 2A growing network capable of generating a fixed number of nodes
dc.contributor.authorHe, J.
dc.contributor.authorTan, A.-H.
dc.contributor.authorTan, C.-L.
dc.date.accessioned2013-07-23T09:24:06Z
dc.date.available2013-07-23T09:24:06Z
dc.date.issued2004
dc.identifier.citationHe, J., Tan, A.-H., Tan, C.-L. (2004). Modified ART 2A growing network capable of generating a fixed number of nodes. IEEE Transactions on Neural Networks 15 (3) : 728-737. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2004.826220
dc.identifier.issn10459227
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/43069
dc.description.abstractThis 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TNN.2004.826220
dc.sourceScopus
dc.subjectAdaptive Resonance Theory (ART)
dc.subjectClustering
dc.subjectConstraint learning
dc.subjectNeural networks
dc.typeArticle
dc.contributor.departmentINSTITUTE OF ENGINEERING SCIENCE
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1109/TNN.2004.826220
dc.description.sourcetitleIEEE Transactions on Neural Networks
dc.description.volume15
dc.description.issue3
dc.description.page728-737
dc.description.codenITNNE
dc.identifier.isiut000221483700016
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