A CLASS OF UPPER BOUNDS ON PROBABILITY OF ERROR FOR MULTI-HYPOTHESES PATTERN RECOGNITION.
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)
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 22, 1969
- Accession Number
- AD0690328
Entities
People
- D. G. Lainiotis
Organizations
- University of Texas at Austin