Parallelization of Support Vector Machines
Name
Olga Agen
Abstract
One of the techniques used for data classification is support vector machine (SVM). SVM takes binary classification as the fundamental
problem and follows the geometrically intuitive approach to find a hyperplane
that divides objects into two separate classes. The training part of the SVM aims to
both maximize the width of the margin that surrounds the separating hyperplane and
minimize the occurrence of classification errors.
The goal of given thesis is to research efficiency in performance gained by using
parallel approach to solve SVM and compare proposed techniques for parallelization
in accuracy and computation speed.
Graduation Thesis language
English
Graduation Thesis type
Master - Information Technology
Supervisor(s)
Oleg Batrashev, Artjom Lind
Defence year
2013