Accession Number:

AD0637486

Title:

NONSUPERVISED PATTERN RECOGNITION THROUGH THE DECOMPOSITION OF PROBABILITY FUNCTIONS.

Descriptive Note:

Scientific technical rept.

Corporate Author:

MICHIGAN UNIV ANN ARBOR SENSORY INTELLIGENCE LAB

Personal Author(s):

Report Date:

1966-04-01

Pagination or Media Count:

62.0

Abstract:

Two problems of parametric statistics are investigated with a view to their application to nonsupervised pattern recognition. Each of the problems can be described as follows given a random sample drawn from a finite mixture of probability functions, where each element of the mixture is of a known parametric form, determine the unknown parameters of the mixture, fX. The problem is treated in two parts. In the first part, it is assumed that the function fX is known and the decomposition of fX into its components is discussed. The second part deals with the estimation of fX on the basis of a random sample drawn according to it. Author

Subject Categories:

  • Statistics and Probability
  • Bionics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE