
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) : Stanat,Donald F.
Report Date : APR 1966
Pagination or Media Count : 62
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, f(X). The problem is treated in two parts. In the first part, it is assumed that the function f(X) is known and the decomposition of f(X) into its components is discussed. The second part deals with the estimation of f(X) on the basis of a random sample drawn according to it. (Author)
Descriptors : (*PATTERN RECOGNITION, LEARNING), (*STATISTICAL FUNCTIONS, PATTERN RECOGNITION), LEARNING, PROBABILITY, STATISTICAL DISTRIBUTIONS, INTEGRAL TRANSFORMS, STEEPEST DESCENT METHOD, NUMERICAL METHODS AND PROCEDURES, SET THEORY
Subject Categories : Statistics and Probability
Bionics
Distribution Statement : APPROVED FOR PUBLIC RELEASE