| 支持向量机对采空区稳定时间的预计 |
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| 引用本文： | 李爱兵,尹彦波.支持向量机对采空区稳定时间的预计[J].岩土力学,2005,26(Z2):231-233. |
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| 作者姓名： | 李爱兵 尹彦波 |
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| 作者单位： | 长沙矿山研究院，长沙 410012 |
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| 摘 要： | 支持向量机是基于结构风险最小化原理的机器学习技术，在广泛收集金属矿山采空区失稳塌陷时间资料的基础上，建立了基于支持向量机的采空区稳定时间的预计模型。通过对采空区稳定时间测试样本的预测研究表明，用支持向量机来预计采空区的稳定时间是可行的。
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| 关 键 词： | 采空区 支持向量机 失稳时间 回归 |
| 收稿时间： | 2005-04-29 |
Estimation of worked-out area stable time by support vector machines |
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| LI Ai-bing,YIN Yan-bo.Estimation of worked-out area stable time by support vector machines[J].Rock and Soil Mechanics,2005,26(Z2):231-233. |
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| Authors: | LI Ai-bing YIN Yan-bo |
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| Institution: | Changsha Institute of Mining Research, Changsha 410012, China |
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| Abstract: | Support vector machine (SVM) is a machine learning technique based on the structural risk minimization principle.Based on collected data of worked-out area stable time from many metallic mines, the model method of SVM is set up for evaluating worked-out area stable time. Through the test to the testing samples, the results show that the SVM for predicting worked-out area stable time is effective. |
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| Keywords: | worked-out area support vector machines stable time regression |
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