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

Robust classification techniques for connection pattern analysis with adaptive decision boundaries using CUDA

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

3 Author(s)
Qureshi, M.N.I. ; Dept. of Comput. Eng., Chosun Univ., Gwangju, South Korea ; Ji-Eun Lee ; Sang Woong Lee

Social networking become an essential part of life in both personal as well as professional relationships. We, therefore implement the pattern analysis of connections in a social media network. For more effective and faster connection suggestions on social networking websites we need to analyze connected networks on the basis of clustering and adaptive decision boundary techniques. The connection suggestion and classification based on minimum computation time in social networks has become an area of major interest. These algorithms occupy a lot of host machine resources to execute the bunch of nested threads that result in the overall speed reduction. Therefore we have seen the GPU is become an essential part of standard internet browsers to speed up the applications. If we want to run a simple decision boundary classifier application in real-time, either a very high speed processor along with bulk of free memory is required or some other parallel computing techniques should be used. In this paper, we are trying to take benefit from NVIDIA's GPU to build a robust classification technique for pattern recognition with adaptive decision boundaries using CUDA to ensure high speed connection suggestions and better network connection analysis.

Published in:

Cloud Computing and Social Networking (ICCCSN), 2012 International Conference on

Date of Conference:

26-27 April 2012

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.