文档介绍：works 18 (2005) 287–295
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anizing information fusion and hierarchical knowledge discovery:
a new framework using ARTMAP works
Gail A. Carpenter*, Siegfried Martens, Ogi J. Ogas
Department of Cognitive and Neural Systems, Center for Adaptive Systems, 677 Beacon Street, Boston University, Boston, MA 02215, USA
Received 8 December 2003; revised 14 December 2004; accepted 14 December 2004
Abstract
Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors working
at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve
inconsistencies, as when evidence variously suggests that an object’s class is car, truck,orairplane. The methods described here 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 automated system
or the human user. The ARTMAP information fusion system uses distributed code representations that exploit the work’s capacity
for one-to-many learning in order to produce anizing expert systems that discover hierarchical knowledge structures. The fusion
system infers multi-level rela