By Topic

Based on support vector machine approach to missing data

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
$33 $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)
Yang Li-hua ; School of Information Engineering, JingDeZhen Ceramic Institute, China ; Nie Qing-hua

This paper systematically analyzes the causes and the mechanism of missing data, and research the processing method of missing data based on the support vector machine. And the results show that the prediction based on support vector machine method is more desirable than neural network, wavelet network model. And this method can promote and apply in the prediction of missing data to a certain extend.

Published in:

System Science, Engineering Design and Manufacturing Informatization (ICSEM), 2011 International Conference on  (Volume:1 )

Date of Conference:

22-23 Oct. 2011