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dc.contributor.authorGrossberg, Stephenen_US
dc.contributor.authorRubin, Mark A.en_US
dc.contributor.authorStreilein, William W.en_US
dc.date.accessioned2011-11-14T19:07:09Z
dc.date.available2011-11-14T19:07:09Z
dc.date.issued1996-05en_US
dc.identifier.urihttp://hdl.handle.net/2144/2313
dc.description.abstractFuzzy ARTMAP has to date been applied to a variety of automatic target recognition tasks, including radar range profile classification. In simulations of this task, it has demonstrated significant compression compared to k-nearest-neighbor classifiers. During supervised learning, match tracking search allocates memory based on the degree of similarity between newly encountered and previously encountered inputs, regardless of their prior predictive success. Here we invesetigate techniques that buffer reset based on a category's previous predictive success and thereby substantially improve the compression achieved with minimal loss of accuracy.en_US
dc.description.sponsorshipOffice of Naval Research (N00014-95-1-0657, N00014-95-1-0409, N00014-96-1-0659)en_US
dc.language.isoen_USen_US
dc.publisherBoston University Center for Adaptive Systems and Department of Cognitive and Neural Systemsen_US
dc.relation.ispartofseriesBUCAS/CNS Technical Reports; BUCAS/CNS-TR-1996-014en_US
dc.rightsCopyright 1996 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.titleBuffered Reset Leads to Improved Compression in Fuzzy ARTMAP Classification of Radar Range Profilesen_US
dc.typeTechnical Reporten_US
dc.rights.holderBoston University Trusteesen_US


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