Application of Support Vector Machine in Coal and Gas Outburst Area Prediction

摘要：

Support vector machine (SVM) is a novel machine learning method based on statistical learning theory (SLT). SVM is powerful for the problem with small samples, 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.

关键词： coal and gas outburst support vector machine forecast outburst classification

作者: Yuping Wu

作者单位: School of Economic and Management,Henan Polytechnic University,Jiaozuo,454000,P.R.China

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(2009 IEEE 智能计算与智能系统国际会议)

会议地点: 上海

会议语种:英文

页码: 2728-2732

在线出版日期: 2009-11-20（万方平台首次上网日期，不代表论文的发表时间）