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

Comments

Degree granted by The University of Texas at Arlington

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