The application of support vector machine in the potentiality evaluation for revegetation of abandoned lands from coal mining activities

Authors:
Chuanli Zhuang;Zetian Fu;Ping Yang;Xiaoshuan Zhang
Affiliations:
College of Economics & Management, China Agricultural University, Beijing, China;Key Laboratory of Modern Precision Agriculture System Integration, Ministry of Education, Beijing, China;College of Economics & Management, China Agricultural University, Beijing, China;College of engineering, China Agricultural University, Beijing, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

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Abstract

This paper presents the comparableness of SVM method to artificial neural networks in the outlier detection problem of high dimensions. Experiments performed on real dataset show that the performance of this method is mostly superior to that of artificial neural networks. The proposed method, SVM served to exemplify that kernel-based learning algorithms can be employed as an efficient method for evaluating the revegetation potentiality of abandoned lands from coal mining activities.