Estimation of worked-out area stable time by support vector machines

LI Ai-bing; YIN Yan-bo

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Estimation of worked-out area stable time by support vector machines

更新时间：2026-06-25

- Estimation of worked-out area stable time by support vector machines
- Vol. 26, Issue S2, (2005)
- 作者机构：
  
  长沙矿山研究院,长沙,410012
- 作者简介：
- 基金信息：
- DOI：
  CLC： TU457
- Online First：16 December 2005，
  
  Published：2005
- 稿件说明：
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LI Ai-bing, YIN Yan-bo. Estimation of worked-out area stable time by support vector machines[J]. 2005, 26(S2).
DOI：

LI Ai-bing, YIN Yan-bo. Estimation of worked-out area stable time by support vector machines[J]. 2005, 26(S2). DOI：

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。

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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