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Online pattern classification with multiple neural network systems: an experimental study
In this paper, an empirical study of the development and application of a committee of neural networks on online pattern classification tasks is presented. A multiple classifier framework is designed by adopting an Adaptive Resonance Theory-based (ART) autonomously learning neural network as the building block. A number of algorithms for combining outputs from multiple neural classifiers are considered, and two benchmark data sets have been used to evaluate the applicability of the proposed system. Different learning strategies coupling offline and online learning approaches, as well as different input pattern representation schemes, including the "ensemble" and "modular" methods, have been examined experimentally. Benefits and shortcomings of each approach are systematically analyzed and discussed. The results are comparable, and in some cases superior, with those from other classification algorithms. The experiments demonstrate the potentials of the proposed multiple neural network systems in offering an alternative to handle online pattern classification tasks in possibly nonstationary environments.
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Journal
IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and ReviewsVolume
33Issue
2Pagination
235 - 247Publisher
Institute of Electrical and Electronics EngineersLocation
Piscataway, N. JPublisher DOI
ISSN
1094-6977eISSN
1558-2442Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2003, IEEEUsage metrics
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No categories selectedKeywords
Adaptive resonance theoryBenchmark studiesDecision combination algorithmsMultiple neural network systemsOnline learningScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, CyberneticsComputer Science, Interdisciplinary ApplicationsComputer ScienceRECOGNITIONARCHITECTUREMIXTURESEXPERTSARTMAP
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