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dc.contributor.authorCarpenter, Gailen_US
dc.date.accessioned2011-11-14T19:00:16Z
dc.date.available2011-11-14T19:00:16Z
dc.date.issued2000-03
dc.identifier.urihttps://hdl.handle.net/2144/2253
dc.description.abstractART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include airplane design and manufacturing, automatic target recognition, financial forecasting, machine tool monitoring, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, Gaussian ARTMAP, and distributed ARTMAP. ARTMAP has been used for a variety of applications, including computer-assisted medical diagnosis. Medical databases present many of the challenges found in general information management settings where speed, efficiency, ease of use, and accuracy are at a premium. A direct goal of improved computer-assisted medicine is to help deliver quality emergency care in situations that may be less than ideal. Working with these problems has stimulated a number of ART architecture developments, including ARTMAP-IC [1]. This paper describes a recent collaborative effort, using a new cardiac care database for system development, has brought together medical statisticians and clinicians at the New England Medical Center with researchers developing expert systems and neural networks, in order to create a hybrid method for medical diagnosis. The paper also considers new neural network architectures, including distributed ART {dART), a real-time model of parallel distributed pattern learning that permits fast as well as slow adaptation, without catastrophic forgetting. Local synaptic computations in the dART model quantitatively match the paradoxical phenomenon of Markram-Tsodyks [2] redistribution of synaptic efficacy, as a consequence of global system hypotheses.en_US
dc.description.sponsorshipOffice of Naval Research (N00014-95-1-0409, N00014-95-1-0657)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-2000-005
dc.rightsCopyright 2000 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.subjectAdaptive Resonance Theory (ART)en_US
dc.subjectARTen_US
dc.subjectARTMAPen_US
dc.subjectNeural networksen_US
dc.subjectMedical predictionen_US
dc.subjectRedistribution of synaptic efficacyen_US
dc.titleART Neural Networks: Distributed Coding and ARTMAP Applicationsen_US
dc.typeTechnical Reporten_US
dc.rights.holderBoston University Trusteesen_US


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