Parallelization of Support Vector Machines

Tugivektormasin (Support Vector Machine) on masinõppe meetod, mida kasutakse andmete klassifitseerimiseks. Binaarse klassifikatsiooni probleem seisneb sellise funktsiooni või mudeli leidmisel, mis oskaks ennustada, mis klassi etteantud punkt x kuulub. Mudeli treenimiseks kasutatakse treeningandmeid. Oma töös võrdlesime iteratiivsed ja paralleelseid tugivektormasina algoritmide implementatsioone. Uurimise käigus avastasime et paralleelsed algoritmid, nagu oligi oodatud, töötavad palju kiiremini kui iteratiivsed, seejuures valesti klassifitseeritud punktide arv ei suurene.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

An Efficient Data Preprocessing Procedure for Support Vector Clustering

This paper presents an efficient data preprocessing procedure for the of support vector clustering (SVC) to reduce the size of a training dataset. Solving the optimization problem and labeling the data points with cluster labels are time-consuming in the SVC training procedure. This makes using SVC to process large datasets inefficient. We proposed a data preprocessing procedure to solve the problem. The procedure contains a shared nearest neighbor (SNN) algorithm, and utilizes the concept of unit vectors for eliminating insignificant data points from the dataset. Computer simulations have been conducted on artificial and benchmark datasets to demonstrate the effectiveness of the proposed method