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The minimum distance approach to classification (1971)

Wacker, A. G. ; Landgrebe, D. A.

In: CASI

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Publication Date: 2019-06-27

Description: The work to advance the state-of-the-art of miminum distance classification is reportd. This is accomplished through a combination of theoretical and comprehensive experimental investigations based on multispectral scanner data. A survey of the literature for suitable distance measures was conducted and the results of this survey are presented. It is shown that minimum distance classification, using density estimators and Kullback-Leibler numbers as the distance measure, is equivalent to a form of maximum likelihood sample classification. It is also shown that for the parametric case, minimum distance classification is equivalent to nearest neighbor classification in the parameter space.

Keywords: MATHEMATICS

Type: NASA-CR-133796 , TR-EE-71-37 , LARS-INFORM-NOTE-100771

Format: application/pdf

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