Masters Theses
Abstract
"In this study the Bayes likelihood detector is combined with an adaptive decision threshold classifier to solve the multicategory pattern recognition problem. It is assumed that the pattern classes can be represented by an n-dimensional vector sample taken from a multivariate gaussian probability distribution. This study presents (1) the derivation of the A̲daptive H̲ypersphere D̲ecision T̲hreshold classifier (AHDT classifier) and shows (2) how the AHDT classifier minimizes the probability of error using the learning patterns. Finally the AHDT classifier is applied to the solution of a physical problem through computer simulation"--Abstract, page ii.
Advisor(s)
Kern, Frank J.
Committee Member(s)
Bertnolli, Edward C.
Antle, Charles E.
Department(s)
Electrical and Computer Engineering
Degree Name
M.S. in Electrical Engineering
Publisher
University of Missouri at Rolla
Publication Date
1968
Pagination
xi, 104 pages
Rights
© 1968 Darroll S. McCormack, All rights reserved.
Document Type
Thesis - Open Access
File Type
text
Language
English
Subject Headings
Adaptive control systems
Pattern perception
Pattern recognition systems
Thesis Number
T 2136
Print OCLC #
5998338
Electronic OCLC #
794240951
Link to Catalog Record
Recommended Citation
McCormack, Darroll Steven, "A method of supervised pattern recognition by an adaptive hypersphere decision threshold" (1968). Masters Theses. 5190.
https://scholarsmine.mst.edu/masters_theses/5190
