Pattern discrimination using ellipsoidally symmetric multivariate density functions

  • Haralick R
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Abstract

A brief review of ellipsoidally symmetric density functions is done. For the case of monotonic functional forms and distributions with common covariance matrices, a lower bound on the probability of correct classification is calculated in terms of either an incomplete beta or gamma integral, for a class of common functional forms. The lower bound is a monotonically increasing function of the Mahalanobis distance for all monotonic ellipsoidally symmetric forms. © 1977.

Author-supplied keywords

  • Ellipsoidally symmetric density function
  • Multivariate density function
  • Pattern discrimination error bounds
  • Statistical pattern discrimination

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Authors

  • Robert M. Haralick

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