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Self-Organizing Information Fusion and Hierarchical Knowledge Discovery: A New Framework Using Artmap Neural Networks

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dc.contributor.author Carpenter, Gail en_US
dc.contributor.author Martens, Siegfried en_US
dc.contributor.author Ogas, Ogi en_US
dc.date.accessioned 2011-11-14T18:17:02Z
dc.date.available 2011-11-14T18:17:02Z
dc.date.issued 2004-12 en_US
dc.identifier.uri http://hdl.handle.net/2144/1936
dc.description.abstract Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors woring at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when eveidence variously suggests that and object's class is car, truck, or airplane. The methods described her address a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the autonomated system or the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierachical knowlege structures. The fusion system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples, but is not limited to image domain. en_US
dc.description.sponsorship Air Force Office of Scientific Research (F49620-01-1-0423); National Geospatial-Intelligence Agency (NMA 201-01-1-2016, NMA 501-03-1-2030); National Science Foundation (SBE-0354378, DGE-0221680); Office of Naval Research (N00014-01-1-0624); Department of Homeland Security 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-2004-016 en_US
dc.rights Copyright 2004 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 ARTMAP en_US
dc.subject Adaptive Resonance Theory (ART) en_US
dc.subject Information fusion en_US
dc.subject Pattern recognition en_US
dc.subject Data mining en_US
dc.subject Remote sensing en_US
dc.subject Distributed coding en_US
dc.subject Association rules en_US
dc.subject Multi-sensor fusion en_US
dc.title Self-Organizing Information Fusion and Hierarchical Knowledge Discovery: A New Framework Using Artmap Neural Networks en_US
dc.type Technical Report en_US
dc.rights.holder Boston University Trustees en_US


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