Semi-supervised learning methods

被引：0
作者：
Liu, Jian-Wei [1 ]
Liu, Yuan [1 ]
Luo, Xiong-Lin [1 ]
机构：
[1] Research Institute of Automation, China University of Petroleum, Beijing
来源：
Jisuanji Xuebao/Chinese Journal of Computers | 2015年 / 38卷 / 08期
基金：
中国国家自然科学基金;
关键词：
Label; Labeled examples; Pair-wise constraints; Semi-supervised learning; Unlabeled instances;
D O I：
10.11897/SP.J.1016.2015.01592
中图分类号：
TP181 [自动推理、机器学习];
学科分类号：
摘要：
Semi-supervised learning is used to study how to improve performance in the presence of both examples and instances, and it has become a hot area of machine learning field. In view of the theoretical significance and practical value of semi-supervised learning, semi-supervised learning methods were reviewed in this paper systematically. Firstly, some concepts about semi-supervised learning were summarized, including definition of semi-supervised learning, development of research, assumptions relied on semi-supervised learning methods and classification of semi-supervised learning. Secondly, semi-supervised learning methods were detailed from four aspects, including classification, regression, clustering, and dimension reduction. Thirdly, theoretical analysis on semi-supervised learning was studied, and error bounds and sample complexity were given. Finally, the future research on semi-supervised learning was discussed. ©, 2015, Science Press. All right reserved.
引用
收藏
页码：1592 / 1617
页数：25
相关论文
共 187 条
[1]  
Chapelle O., Scholkopf B., Zien A., Semi-Supervised Learning, (2006)
[2]  
Zhu X., Semi-supervised learning literature survey, (2006)
[3]  
Zhou Z.H., Li M., Semi-supervised learning by disagreement, Knowledge and Information Systems, 24, 3, pp. 415-439, (2010)
[4]  
Wang W., Zhou Z.H., Analyzing co-training style algorithms, Proceedings of the 18th European Conference on Machine Learning (ECML'07), pp. 454-465, (2007)
[5]  
Yarowsky D., Unsupervesed word sense disambiguation rivaling supervised methods, Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, pp. 189-195, (1995)
[6]  
Scudder H.J., Probability of error of some adaptive pattern-recognition machines, IEEE Transductions on Information Theory, 11, 3, pp. 363-371, (1965)
[7]  
Fralick S., Learning to recognize patterns without a teacher, IEEE Transductions on Information Theory, 13, 1, pp. 57-64, (1967)
[8]  
Agrawala A.K., Learning with a probabilistic teacher, IEEE Transductions on Information Theory, 16, 4, pp. 373-379, (1970)
[9]  
Merz C.J., St. Clair D.C., Bond W.E., Semi-supervised adaptive resonance theory, Proceedings of the 1992 International Joint Conference on Neural Networks, pp. 851-856, (1992)
[10]  
Shahshahani B.M., Landgrebe D.A., The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon, IEEE Transactions on Geoscience and Remote Sensing, 32, 5, pp. 1087-1095, (1994)
12345678910