Expectation maximization and its application in modeling, segmentation and anomaly detection
"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.
Agarwal, Sanjeev, 1971-
Samaranayake, V. A.
Stanley, R. Joe
Electrical and Computer Engineering
M.S. in Electrical Engineering
University of Missouri--Rolla
ix, 108 leaves
© 2006 Ritesh Ganju, All rights reserved.
Thesis - Citation
Library of Congress Subject Headings
Land mines -- Detection -- Mathematical models
Pattern recognition systems -- Mathematical models
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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
Ganju, Ritesh, "Expectation maximization and its application in modeling, segmentation and anomaly detection" (2006). Masters Theses. 5870.