By Topic

Forecasting Coal and Gas Outburst Based on Support Vector Machine

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)
Yongbao Wang ; Henan Polytech. Univ., Jiaozuo, China ; Yong Zhao

Support vector machine (SVM) is a novel machine learning method based on statistical learning theory (SLT). SVM is powerful for the problem with small sample, non linear and high dimension. A multi-class SVM classifier is applied to predict the coal and gas outburst in the paper. In this model, the dominant factors are the input vectors and the degree of outburst danger is divided into four types: heavy outburst, common outburst, outburst warning and no existing outburst. Through a special data dealing process, the multi-class SVM classifier, trained with the sampling data, identifies out the four types of coal and gas outburst states. An empirical analysis shows that some perfect computing conclusions have been acquired by the proposed model.

Published in:

Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on

Date of Conference:

19-20 Dec. 2009