Parallelization of Support Vector Machines
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
Graduation Thesis type
Master - Information Technology
Oleg Batrashev, Artjom Lind