ORCID Identifier(s)

0000-0002-5301-9368

Graduation Semester and Year

2016

Language

English

Document Type

Thesis

Degree Name

Master of Science in Electrical Engineering

Department

Electrical Engineering

First Advisor

Michael T Manry

Abstract

Nearest Neighbor algorithms are non-parametric algorithms that use distance measure techniques for classification and regressions. This thesis, first improves the traditional nearest neighbor classifier by optimizing its distance measure using a second order training algorithm. It then presents a second order method to adjust center vectors. It is shown that the distance measure weight optimization and center vector optimization, individually and together reduce the final classification error. The testing error of our algorithm is shown to be less than that of LVQ V.2.1

Keywords

Weighted nearest neighbor classifier, Weighted Euclidean distance, Center vector optimization

Disciplines

Electrical and Computer Engineering | Engineering

Comments

Degree granted by The University of Texas at Arlington

26430-2.zip (776 kB)

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