Accession Number : AD0690328
Title : A CLASS OF UPPER BOUNDS ON PROBABILITY OF ERROR FOR MULTI-HYPOTHESES PATTERN RECOGNITION.
Descriptive Note : Technical memo.,
Corporate Author : TEXAS UNIV AUSTIN ELECTRONICS RESEARCH CENTER
Personal Author(s) : Lainiotis,D. G.
Report Date : 22 APR 1969
Pagination or Media Count : 11
Abstract : A class of upper bounds on the probability of error for the general multi-hypotheses pattern recognition problem is obtained. In particular, an upper bound in the class is shown to be a linear functional of the pairwise Bhattacharya coefficients. Evaluation of the bounds requires knowledge of a priori probabilities and of the hypothesis-conditional probability density functions. A further bound is obtained that is independent of a priori probabilities. For the case of unknown a priori probabilities and conditional probability densities, an estimate of the latter upper bound is derived using a sequence of classified samples and Kernel functions to destimate the unknown densities. (Author)
Descriptors : (*PATTERN RECOGNITION, INFORMATION THEORY), PROBABILITY DENSITY FUNCTIONS, DETECTION, ERRORS, THEOREMS
Subject Categories : Cybernetics
Distribution Statement : APPROVED FOR PUBLIC RELEASE