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Abstract
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Article Information:
Rock Burst Monitoring and Early Warning Based on Incremental Learning Method with SVM
Chunfang Wu, Ruisheng Jia and Tao Qiu
Corresponding Author: Chunfang Wu
Submitted: October 04, 2013
Accepted: October 22, 2013
Published: December 01, 2013 |
Abstract:
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The rock burst hazard is a common geological hazard. In this study, we investigate an approach for classification of rock burst situation. This study relies on support vector machine classifier which in the case of less prior knowledge, still has the ability of classification. First we describes the current research work on rock burst monitoring and early warning and reasons for the introduction of support vector machines and later propose support vector machines algorithm and its improvement strategies. The results illustrate that incremental learning method for support vector machine not only requires less prior knowledge, but also without affecting the performance at the same time and training time will be substantially reduced. The method for rock burst monitoring and early warning has exhibited remarkable detection and generalization performance.
Key words: classification, early warning, incremental learning method, monitoring, rock burst hazard, svm classification,
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Cite this Reference:
Chunfang Wu, Ruisheng Jia and Tao Qiu, . Rock Burst Monitoring and Early Warning Based on Incremental Learning Method with SVM. Research Journal of Information Technology , (4): 121-124.
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ISSN (Online): 2041-3114
ISSN (Print): 2041-3106 |
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