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     Research Journal of Information Technology


Rock Burst Monitoring and Early Warning Based on Incremental Learning Method with SVM

Chunfang Wu, Ruisheng Jia and Tao Qiu
College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China
Research Journal of Information Technology  2013  4:121-124
http://dx.doi.org/10.19026/rjit.5.5797  |  © The Author(s) 2013
Received: October 04, 2013  |  Accepted: October 22, 2013  |  Published: December 01, 2013

Abstract

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.

Keywords:

Classification , early warning, incremental learning method, monitoring, rock burst hazard, SVM classification,


References


Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2041-3114
ISSN (Print):   2041-3106
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