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Buffered Reset Leads to Improved Compression in Fuzzy ARTMAP Classification of Radar Range Profiles

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dc.contributor.author Grossberg, Stephen en_US
dc.contributor.author Rubin, Mark A. en_US
dc.contributor.author Streilein, William W. en_US
dc.date.accessioned 2011-11-14T19:07:09Z
dc.date.available 2011-11-14T19:07:09Z
dc.date.issued 1996-05 en_US
dc.identifier.uri http://hdl.handle.net/2144/2313
dc.description.abstract Fuzzy 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.sponsorship Office of Naval Research (N00014-95-1-0657, N00014-95-1-0409, N00014-96-1-0659) 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 BUCAS/CNS Technical Reports; BUCAS/CNS-TR-1996-014 en_US
dc.rights Copyright 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.title Buffered Reset Leads to Improved Compression in Fuzzy ARTMAP Classification of Radar Range Profiles en_US
dc.type Technical Report en_US
dc.rights.holder Boston University Trustees en_US


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