NONSUPERVISED ADAPTIVE DETECTION FOR MULTIVARIATE NORMAL DISTRIBUTIONS.

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)

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1966
Accession Number
AD0640494

Entities

People

  • Paul W. Cooper

Organizations

  • Sylvania Electric Products

Tags

DTIC Thesaurus Topics

  • Classification
  • Covariance
  • Detection
  • Learning
  • Mathematics
  • Normal Distribution
  • Pattern Recognition
  • Probability
  • Probability Distributions
  • Recognition
  • Sequences
  • Signal Detection

Fields of Study

  • Mathematics

Readers

  • Neural Network Machine Learning.
  • Phased Array Antenna Design.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms