Performance evaluation of hybrid implementation of support vector machine

Authors:
Konrad Gajewski;Michal Wozniak
Affiliations:
Department of Systems and Computer Networks, Wroclaw University of Technology, Wroclaw, Poland;Department of Systems and Computer Networks, Wroclaw University of Technology, Wroclaw, Poland
Venue:
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Year:
2012

Citing 9
Cited 0

Making large-scale support vector machine learning practical

Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Scalable Parallel Programming with CUDA

Queue - GPU Computing
NVIDIA Tesla: A Unified Graphics and Computing Architecture

IEEE Micro
Fast support vector machine training and classification on graphics processors

Proceedings of the 25th international conference on Machine learning
Introduction to Machine Learning

Introduction to Machine Learning
Complexity and multithreaded implementation analysis of one class-classifiers fuzzy combiner

HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Which is the best multiclass SVM method? an empirical study

MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Parallel sequential minimal optimization for the training of support vector machines

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This article focuses on the problem how to shorten the time required for training and decision making by classifiers based on Support Vector Machines techniques. We propose the hybrid implementation of mentioned above algorithm which uses parallel implementation of SVM based on GPU programming model in a distributed computing system using MPI protocol. To estimate the computational efficiency of the proposed model a number of experiments were carried out on the basis of UCI benchmark datasets. Their results show that using parallel model in distributed computing environment can reduce computation time compared to both classical SVM used single processor only and to SVM implementation based on GPU.