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Multiclass Bayes error estimation by a feature space sampling techniqueA general Gaussian M-class N-feature classification problem is defined. An algorithm is developed that requires the class statistics as its only input and computes the minimum probability of error through use of a combined analytical and numerical integration over a sequence simplifying transformations of the feature space. The results are compared with those obtained by conventional techniques applied to a 2-class 4-feature discrimination problem with results previously reported and 4-class 4-feature multispectral scanner Landsat data classified by training and testing of the available data.
Document ID
19800030230
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Mobasseri, B. G.
(Purdue Univ. West Lafayette, IN, United States)
Mcgillem, C. D.
(Purdue University West Lafayette, Ind., United States)
Date Acquired
August 10, 2013
Publication Date
October 1, 1979
Subject Category
Statistics And Probability
Accession Number
80A14400
Funding Number(s)
CONTRACT_GRANT: NAS9-14970
Distribution Limits
Public
Copyright
Other

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