Item Details

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