Accession Number : ADA132237
Title : On a Relation between Maximum-Likelihood Classification and Minimum-Cross-Entropy Classification.
Descriptive Note : Interim rept.,
Corporate Author : NAVAL RESEARCH LAB WASHINGTON DC
Personal Author(s) : Shore,John E
PDF Url : ADA132237
Report Date : 14 Jul 1983
Pagination or Media Count : 7
Abstract : The report considers maximum likelihood (ML) and minimum cross entropy (MCE)classification of samples from an unknown probability density when the hypotheses comprise an exponential family. It is shown that ML and MCE lead to the same classification rule, and the result is illustrated in terms of method for estimating covariance matrices recently developed by Burg, Luenberger, and Wenger. MCE classification applies to the general case in which it cannot be assumed that the samples were generated by one of the hypothesis densities. The common use of ML in this case is technically incorrect, but the equivalence of MCE and ML provides a theoretical justification.
Descriptors : *Entropy, *Maximum likelihood estimation, Classification, Signal processing, Spectral energy distribution, Mathematical analysis, Predictions, Information theory, Spectrum analysis
Subject Categories : Information Science
Distribution Statement : APPROVED FOR PUBLIC RELEASE