OpenBU

Adaptive Resonance Theory: Self-Organizing Networks for Stable Learning, Recognition, and Prediction

OpenBU

Show simple item record

dc.contributor.author Carpenter, Gail en_US
dc.contributor.author Grossberg, Stephen en_US
dc.date.accessioned 2011-11-14T18:50:15Z
dc.date.available 2011-11-14T18:50:15Z
dc.date.issued 1995-05 en_US
dc.identifier.uri http://hdl.handle.net/2144/2194
dc.description.abstract Adaptive 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.sponsorship Office 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.iso en_US en_US
dc.publisher Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems en_US
dc.relation.ispartofseries BU CAS/CNS Technical Reports;CAS/CNS-TR-1995-017 en_US
dc.rights Copyright 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.subject Adaptive resonance theory en_US
dc.subject ART en_US
dc.subject Neural network en_US
dc.subject Unsupervised learning en_US
dc.subject Supervised learning en_US
dc.subject Patern recognition en_US
dc.subject Categorization en_US
dc.subject Attention en_US
dc.subject Prototype en_US
dc.subject Fuzzy logic en_US
dc.subject Production system en_US
dc.subject Vision en_US
dc.subject Audition en_US
dc.title Adaptive Resonance Theory: Self-Organizing Networks for Stable Learning, Recognition, and Prediction en_US
dc.type Technical Report en_US
dc.rights.holder Boston University Trustees en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search OpenBU


Browse

Deposit Materials

Statistics