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dc.contributor.authorCarpenter, Gailen_US
dc.contributor.authorGrossberg, Stephenen_US
dc.date.accessioned2011-11-14T18:50:15Z
dc.date.available2011-11-14T18:50:15Z
dc.date.issued1995-05en_US
dc.identifier.urihttp://hdl.handle.net/2144/2194
dc.description.abstractAdaptive Resonance Theory (ART) is a neural theory of human and primate information processing and of adaptive pattern recognition and prediction for technology. Biological applications to attentive learning of visual recognition categories by inferotemporal cortex and hippocampal system, medial temporal amnesia, corticogeniculate synchronization, auditory streaming, speech recognition, and eye movement control are noted. ARTMAP systems for technology integrate neural networks, fuzzy logic, and expert production systems to carry out both unsupervised and supervised learning. Fast and slow learning are both stable response to large non stationary databases. Match tracking search conjointly maximizes learned compression while minimizing predictive error. Spatial and temporal evidence accumulation improve accuracy in 3-D object recognition. Other applications are noted.en_US
dc.description.sponsorshipOffice of Naval Research (N00014-95-I-0657, N00014-95-1-0409, N00014-92-J-1309, N00014-92-J4015); National Science Foundation (IRI-94-1659)en_US
dc.language.isoen_USen_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-1995-017en_US
dc.rightsCopyright 1995 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)
dc.subjectART
dc.subjectNeural networks
dc.subjectUnsupervised learning
dc.subjectSupervised learning
dc.subjectPatern recognition
dc.subjectCategorization
dc.subjectAttention
dc.subjectPrototype
dc.subjectFuzzy logic
dc.subjectProduction system
dc.subjectVision
dc.subjectAudition
dc.titleAdaptive Resonance Theory: Self-Organizing Networks for Stable Learning, Recognition, and Predictionen_US
dc.typeTechnical Reporten_US
dc.rights.holderBoston University Trusteesen_US


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