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dc.contributor.authorCarpenter, Gail A.en_US
dc.contributor.authorMilenova, Boriana L.en_US
dc.contributor.authorNoeske, Benjamin W.en_US
dc.date.accessioned2011-11-14T18:26:03Z
dc.date.available2011-11-14T18:26:03Z
dc.date.issued1997-12
dc.identifier.urihttps://hdl.handle.net/2144/2138
dc.description.abstractDistributed coding at the hidden layer of a multi-layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off-line learning to avoid catastrophic forgetting in an open input environment. An adaptive resonance theory (ART) model is designed to guarantee stable memories even with fast on-line learning. However, ART stability typically requires winner-take-all coding, which may cause category proliferation in a noisy input environment. Distributed ARTMAP (dARTMAP) seeks to combine the computational advantages of MLP and ART systems in a real-time neural network for supervised learning, An implementation algorithm here describes one class of dARTMAP networks. This system incorporates elements of the unsupervised dART model as well as new features, including a content-addressable memory (CAM) rule for improved contrast control at the coding field. A dARTMAP system reduces to fuzzy ARTMAP when coding is winner-take-all. Simulations show that dARTMAP retains fuzzy ARTMAP accuracy while significantly improving memory compression.en_US
dc.description.sponsorshipNational Science Foundation (IRI-94-01659); Office of Naval Research (N00014-95-1-0409, N00014-95-0657)en_US
dc.language.isoen_US
dc.publisherBoston University Center for Adaptive Systems and Department of Cognitive and Neural Systemsen_US
dc.relation.ispartofseriesBU CAS/CNS Technical Reports;CAS/CNS-TR-1997-026
dc.rightsCopyright 1997 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission.en_US
dc.subjectDistributed ARTMAPen_US
dc.subjectAdaptive Resonance Theory (ART)en_US
dc.subjectARTen_US
dc.subjectARTMAPen_US
dc.subjectDistributed codingen_US
dc.subjectFast learningen_US
dc.subjectSupervised learningen_US
dc.subjectNeural networksen_US
dc.titledARTMAP: A Neural Network for Fast Distributed Supervised Learningen_US
dc.typeTechnical Reporten_US
dc.rights.holderBoston University Trusteesen_US


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