基于隐含信息的半监督学习方法研究  

Study of implicit information semi-supervised learning algorithm

作　　者：刘国栋[1] 许静[1] 张国兵[2] 

机构地区：[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　　处：《通信学报》2015年第10期133-139,共7页Journal on Communications

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。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.

关 键 词：半监督学习 肺音 隐含信息 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]