Masters Theses

Abstract

"The classification of signals through the use of pattern recognition techniques may be viewed as a statistical classification problem since at least some pattern classes are analog signals which have been contaminated by additive noise. In order to represent these signals at the input of a pattern classifier they are sampled and a vector and an m-dimensional vector space used to describe the pattern. Consequently, these pattern samples are processed to determine the parameters of the statistics. In this study the additive noise was assumed to be Gaussian in nature. This study was based on a priori knowledge of the learning samples. This is to say, learning with a teacher was investigated. The three classes of patterns used in the experimental program were generated through computer simulation by adding normally distributed random numbers to previously generated signal classes. The sampled signals thus generated were classified by means of maximum likelihood detectors. The study was divided into two parts. In the first part the statistics of the noise were calculated and the patterns were classified on the basis of these statistics with no learning taking place. In phase two of the study, learning behavior was observed when a sequential calculation was made to determine the noise statistics. It was shown that the probability of mis-recognition of a given pattern asymptotically approached the theoretical minimum as the number of learning samples increased. The probability of the misrecognition of the patterns was also considered as a function of signal to noise ratio and correlation between patterns. A theoretical prediction of this probability of misrecognition was compared with the results of simulation and found to agree closely"--Abstract, page v-vi.

Advisor(s)

Kern, Frank J.

Committee Member(s)

Butler, Edgar L.
Noack, Thomas L.
Bain, Lee J., 1939-

Department(s)

Electrical and Computer Engineering

Degree Name

M.S. in Electrical Engineering

Comments

Leaves 15 and 32 are missing from the print text.

Publisher

University of Missouri at Rolla

Publication Date

1967

Pagination

viii, 50 pages

Note about bibliography

Includes bibliographical references (page 179).

Rights

© 1967 Shaw Yih Chung, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Subject Headings

Computer vision -- EvaluationPattern recognition systems

Thesis Number

T 2012

Print OCLC #

5987559

Electronic OCLC #

793386671

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