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ART 2-A: An Adaptive Resonance Algorithm for Rapid Category Learning and Recognition

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dc.contributor.author Carpenter, Gail A. en_US
dc.contributor.author Grossberg, Stephen en_US
dc.contributor.author Rosen, David en_US
dc.date.accessioned 2011-11-14T18:21:47Z
dc.date.available 2011-11-14T18:21:47Z
dc.date.issued 1991-02 en_US
dc.identifier.uri http://hdl.handle.net/2144/2066
dc.description.abstract This article introduces ART 2-A, an efficient algorithm that emulates the self-organizing pattern recognition and hypothesis testing properties of the ART 2 neural network architecture, but at a speed two to three orders of magnitude faster. Analysis and simulations show how the ART 2-A systems correspond to ART 2 dynamics at both the fast-learn limit and at intermediate learning rates. Intermediate learning rates permit fast commitment of category nodes but slow recoding, analogous to properties of word frequency effects, encoding specificity effects, and episodic memory. Better noise tolerance is hereby achieved without a loss of learning stability. The ART 2 and ART 2-A systems are contrasted with the leader algorithm. The speed of ART 2-A makes practical the use of ART 2 modules in large-scale neural computation. en_US
dc.description.sponsorship BP (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-90-00530); Air Force Office of Scientific Research (90-0175, 90-0128); Army Research Office (DAAL-03-88-K0088) 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-1991-011 en_US
dc.rights Copyright 1991 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 Neural networks en_US
dc.subject Pattern recognition en_US
dc.subject Category formation en_US
dc.subject Fast learning en_US
dc.subject ART en_US
dc.title ART 2-A: An Adaptive Resonance Algorithm for Rapid Category Learning and Recognition en_US
dc.type Technical Report en_US
dc.rights.holder Boston University Trustees en_US


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