Accession Number:

AD0640494

Title:

NONSUPERVISED ADAPTIVE DETECTION FOR MULTIVARIATE NORMAL DISTRIBUTIONS.

Descriptive Note:

Final rept., Feb 65-Apr 66,

Corporate Author:

SYLVANIA ELECTRONIC SYSTEMS-EAST WALTHAM MASS

Personal Author(s):

Report Date:

1966-09-01

Pagination or Media Count:

84.0

Abstract:

Nonsupervised adaptive detection for categories described statistically by multivariate normal distributions is approached as a problem in multi-parameter estimation for a multi-modal distribution. Nonsupervised learning consists in estimating the component probability distributions of a mixture of distributions, given a sequence of samples known only to have been drawn from the over-all mixture. This report considers the two-category case involving general unequal covariance matrices and the multiple-category case for spherically symmetric distributions. The techniques provided are applicable to problems in statistical classification, pattern recognition, and signal detection. Author

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE