摘要
针对支持向量机要求输入向量为已标记样本,而实际应用中已标记样本很难获取的问题,提出将半监督学习和支持向量机结合的煤与瓦斯突出预测方法;介绍了采用SVM预测煤与瓦斯突出的流程及其输入向量的选择;对半监督学习中的协同训练算法进行了改进:在同一属性集上训练2个不同分类器SVM和KNN,将2个分类器标记一致的样本加入训练集,从而充分利用未标记样本不断补充信息,更新训练集标记样本,达到强化训练集的目的。测试结果表明,改进后的算法比单独的支持向量机预测方法准确率更高。
For the problem that support vector machine requires input vectors are labeled samples which are difficult to obtain in practical application, the paper proposed a prediction method of coal and gas outburst combining semi-supervised learning and support vector machine. It introduced prediction process of support vector machine and selection of input vectors, and improved co-training algorithm of semisupervised learning： train two different classifiers of SVM and KNN in the same attributes set, and add the samples with the same label in the training set, so as to take full advantage of unlabeled samples to add information continuously, update labeled samples in the training set, and achieve purpose of intensifying training sets. The test result shows that the improved algorithm has higher accuracy than the prediction method using separate support vector machine.
出处
《工矿自动化》
北大核心
2012年第11期40-42,共3页
Journal Of Mine Automation
基金
国家自然科学基金项目(60974126)
关键词
煤与瓦斯突出预测
支持向量机
半监督学习
协同训练
prediction of coal and gas outburst, support vector machine, semi-supervised learning, cotraining
