Study of implicit information semi-supervised learning algorithm

Guo-dong LIU; Jing XU; Guo-bing ZHANG

doi:10.11959/j.issn.1000-436x.2015263

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Study of implicit information semi-supervised learning algorithm

academic paper | 更新时间：2024-06-05

- Study of implicit information semi-supervised learning algorithm
- Journal on Communications Vol. 36, Issue 10, Pages: 133-139(2015)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-436x.2015263
  CLC： TP181
- Online First：2015-10，
  
  Published：25 October 2015
- 稿件说明：
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Guo-dong LIU, Jing XU, Guo-bing ZHANG. Study of implicit information semi-supervised learning algorithm[J]. Journal on Communications, 2015, 36(10): 133-139.
DOI：

Guo-dong LIU, Jing XU, Guo-bing ZHANG. Study of implicit information semi-supervised learning algorithm[J]. Journal on Communications, 2015, 36(10): 133-139. DOI： 10.11959/j.issn.1000-436x.2015263.

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。

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.

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references

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