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dc.contributor.authorCarpenter, Gailen_US
dc.contributor.authorMartens, Siegfrieden_US
dc.contributor.authorOgas, Ogien_US
dc.date.accessioned2011-11-14T18:17:02Z
dc.date.available2011-11-14T18:17:02Z
dc.date.issued2004-12en_US
dc.identifier.urihttp://hdl.handle.net/2144/1936
dc.description.abstractClassifying 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.sponsorshipAir 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 Securityen_US
dc.language.isoen_USen_US
dc.publisherBoston University Center for Adaptive Systems and Department of Cognitive and Neural Systemsen_US
dc.relation.ispartofseriesBU CAS/CNS Technical Reports;CAS/CNS-TR-2004-016en_US
dc.rightsCopyright 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.subjectARTMAPen_US
dc.subjectAdaptive Resonance Theory (ART)en_US
dc.subjectInformation fusionen_US
dc.subjectPattern recognitionen_US
dc.subjectData miningen_US
dc.subjectRemote sensingen_US
dc.subjectDistributed codingen_US
dc.subjectAssociation rulesen_US
dc.subjectMulti-sensor fusionen_US
dc.titleSelf-Organizing Information Fusion and Hierarchical Knowledge Discovery: A New Framework Using Artmap Neural Networksen_US
dc.typeTechnical Reporten_US
dc.rights.holderBoston University Trusteesen_US


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