Cart (Loading....) | Create Account
Close category search window
 

A Novel Method for Training Large Scale E-Business SVM Models in a Grid Computing Environment

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Qin Hua ; Sch. of Comput. & Electron. Inf., Guangxi Univ., Nanning, China ; Xu Yan-zi

The Support Vector Machines (SVM) become popular E-Business data mining tools recently, and the datasets of E-Business are usually large-scale. If Support Vector Machines are trained on large-scale datasets, the training time will be very long and the classifier's accuracy will become lower too. As training a large-scale SVM is equated to solve a large-scale quadratic programming (QP) problem, so Path Following Interior Point Method (IPM) that can efficiently solve large scale QP problem in polynomial time is proposed to construct a new SVM learning algorithm on large-scale datasets. To improve the SVM learning efficiency, the dimensions of IPM direction equations are degraded first, then LDLT parallel decomposition method is used to solve the direction sub-equations efficiently, and the parallel algorithm is implemented in the ProActive grid-computing environment. The experiment results show that the new parallel SVM training algorithm is efficient and the SVM classifying accuracy is higher than libsvm.

Published in:

E-Business and E-Government (ICEE), 2010 International Conference on

Date of Conference:

7-9 May 2010

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.