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Consensus theoretic classification methodsConsensus theory is adopted as a means of classifying geographic data from multiple sources. The foundations and usefulness of different consensus theoretic methods are discussed in conjunction with pattern recognition. Weight selections for different data sources are considered and modeling of non-Gaussian data is investigated. The application of consensus theory in pattern recognition is tested on two data sets: 1) multisource remote sensing and geographic data and 2) very-high-dimensional remote sensing data. The results obtained using consensus theoretic methods are found to compare favorably with those obtained using well-known pattern recognition methods. The consensus theoretic methods can be applied in cases where the Gaussian maximum likelihood method cannot. Also, the consensus theoretic methods are computationally less demanding than the Gaussian maximum likelihood method and provide a means for weighting data sources differently.
Document ID
19930049023
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Benediktsson, Jon A.
(Univ. of Iceland Reykjavik, United States)
Swain, Philip H.
(Purdue Univ. West Lafayette, IN, United States)
Date Acquired
August 16, 2013
Publication Date
August 1, 1992
Publication Information
Publication: IEEE Transactions on Systems, Man, and Cybernetics
Volume: 22
Issue: 4
ISSN: 0018-9472
Subject Category
Cybernetics
Accession Number
93A33020
Funding Number(s)
CONTRACT_GRANT: NAGW-925
Distribution Limits
Public
Copyright
Other

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