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Performance Analysis of Parallel Support Vector Machines on a MapReduce Architecture

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abstract
The quantity of electronic data available for analysis has grown exponentially with the rapid development of the World Wide Web, the Internet of Things, and other digital technologies. As a result, data mining and machine learning algorithms face computational complexity issues when applied to real world datasets. Support Vector Machines (SVM) are powerful classification and regression tools but their computational requirements increase rapidly as the number of training examples increases. To address this problem, several parallel MapReduce based implementations of SVMs have been proposed. These implementation have in common that they decompose a large-scale multi-class problem to a number of relatively smaller subproblems by dividing the data into multiple partitions which can be processed in parallel; however, these approaches use different aggregation and combination strategies to form the final model.
subject
Machine Learning
Mapreduce
MNIST
Parallel Support Vector Machine
SVM
Udita Patel
contributor
Patel, Udita (author)
Thomas, Stan J (committee chair)
Turkett, William H (committee member)
date
2016-05-21T08:35:51Z (accessioned)
2018-05-20T08:30:10Z (available)
2016 (issued)
degree
Computer Science (discipline)
embargo
2018-05-20 (terms)
identifier
http://hdl.handle.net/10339/59315 (uri)
language
en (iso)
publisher
Wake Forest University
title
Performance Analysis of Parallel Support Vector Machines on a MapReduce Architecture
type
Thesis

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