通信学报 ›› 2015, Vol. 36 ›› Issue (10): 133-139.doi: 10.11959/j.issn.1000-436x.2015263

• 学术论文 • 上一篇    下一篇

基于隐含信息的半监督学习方法研究

刘国栋1,许静1,张国兵2   

  1. 1 南开大学 计算机与控制工程学院,天津 300071
    2 北京航空航天大学 电子信息工程学院,北京 100191
  • 出版日期:2015-10-25 发布日期:2015-10-27

Study of implicit information semi-supervised learning algorithm

Guo-dong LIU1,Jing XU1,Guo-bing ZHANG2   

  1. 1 College of Computer and Control Engineering,Nankai University,Tianjin 300071,China
    2 School of Electronic and Information Engineering,Beihang University,Beijing 100191,China
  • Online:2015-10-25 Published:2015-10-27

摘要:

研究了基于隐含信息的半监督学习方法,并将该方法应用于支持向量机和随机森林模型。利用UCI数据库中的数据验证了基于此方法的支持向量机和随机森林的精度。在此基础上,将此种方法应用于肺音识别领域,利用实际的肺音数据对此方法处理实际问题的效果进行了验证,同时实验分析了无标记样本的数量以及质量对此方法的影响。

关键词: 半监督学习, 肺音, 隐含信息

Abstract:

Implicit information semi supervised learning algorithm was studied.The implicit information semi supervised learning algorithm was used in support vector machine and random forest,which were called semi-SVM and semi-RF.The semi-SVM and semi-RF were evaluated by using UCI,the experimental results show that the semi-SVM and semi-RF are more effective and more precise.The semi-SVM and semi-RF were applied to classifying lung sounds,and verified the effect by using the actual lung sounds data.the quantity and quality of samples affect semi-SVM and semi-RF were analyzed.

Key words: semi-supervised learning, lung sounds, implicit information

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