Graduation Semester and Year
2017
Language
English
Document Type
Thesis
Degree Name
Master of Science in Electrical Engineering
Department
Electrical Engineering
First Advisor
Michael T Manry
Abstract
A new method for training Gaussian Mixture Model (GMM) classifiers is presented. First, an objective function is defined in terms of the number of clusters, K, per class, the mean vectors, the inverse covariance matrices for each class, and the prior probabilities for each class. For each increment in K, gradients of the objective function improve upon the prior probabilities, mean vectors, and inverse covariance matrices. Improvement in accuracy for different data-sets are shown and results are compared with the EM algorithm.
Keywords
Gaussian mixture model, Classifiers
Disciplines
Electrical and Computer Engineering | Engineering
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Gundecha, Vineet Dilip, "A supervised approach for training Gaussian Mixture Model classifiers" (2017). Electrical Engineering Theses. 347.
https://mavmatrix.uta.edu/electricaleng_theses/347
Comments
Degree granted by The University of Texas at Arlington