Masters Theses

Title

Expectation maximization and its application in modeling, segmentation and anomaly detection

Author

Ritesh Ganju

Abstract

"Expectation Maximization (EM) is a general purpose algorithm for solving maximum likelihood estimation problems in a wide variety of situations best described as incomplete data problems. The incompleteness of the data may arise due to missing data, truncated distributions, etc. One such case is a mixture model, where the class association of the data is unknown. In these models, the EM algorithm is used to estimate the parameters of parametric mixture distributions along with the probabilities of occurrence. In this thesis, the EM algorithm is employed to estimate different mixture models for raw single and multi-band electro-optical Infra Red (IF) data"--Abstract, leaf iii.

Advisor(s)

Agarwal, Sanjeev, 1971-

Committee Member(s)

Samaranayake, V. A.
Stanley, R. Joe

Department(s)

Electrical and Computer Engineering

Degree Name

M.S. in Electrical Engineering

Publisher

University of Missouri--Rolla

Publication Date

Spring 2006

Pagination

ix, 108 leaves

Note about bibliography

Includes bibliographical references (page 43).

Rights

© 2006 Ritesh Ganju, All rights reserved.

Document Type

Thesis - Citation

File Type

text

Language

English

Library of Congress Subject Headings

Expectation-maximization algorithms
Gaussian distribution
Land mines -- Detection -- Mathematical models
Pattern recognition systems -- Mathematical models

Thesis Number

T 9074

Print OCLC #

123550980

Link to Catalog Record

Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.

http://laurel.lso.missouri.edu/record=b5846825~S5

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