The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Analyzing the effectiveness and applicability of co-training
Open accessAbstract
Recently there has been signi cant i n terest in supervised learning algorithms that combine labeled and unlabeled data for text learning tasks.The co-training setting [1] applies to datasets that have a natural separation of their features into two disjoint sets.We demonstrate that when learning from labeled and unlabeled data, algorithms explicitly leveraging a natural independent split of the features outperform algorithms that do not.When a natural split does not exist, co-training algorithms that manufacture a feature split may out-perform algorithms not using a split.These results help explain why co-training algorithms are both discriminative in nature and robust to the assumptions of their embedded classi ers.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Text classification from labeled and unlabeled documents using EM
Nigam, K., Mccallum, A. K., Thrun, S., & Mitchell, T. (2000) · Machine Learning · doi:10.1023/a:1007692713085 · Cited by 2,736
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Data Mining: Practical Machine Learning Tools and Techniques
(2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,437
A survey on semi-supervised learning
van Engelen, J. E., & Hoos, H. H. (2020) · Machine Learning · doi:10.1007/s10994-019-05855-6 · Cited by 2,666
Unsupervised deep embedding for clustering analysis
Xie, J., Girshick, R., & Farhadi, A. (2015) · arXiv · doi:10.48550/arxiv.1511.06335 · Cited by 2,476
Classification in the Presence of Label Noise: A Survey
Frenay, B., & Verleysen, M. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2292894 · Cited by 1,777
Introduction to Semi-Supervised Learning
Zhu, X., & Goldberg, A. B. (2009) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.1007/978-3-031-01548-9 · Cited by 1,766
Metadata
- Authors
- Nigam, K., & Ghani, R.
- Venue
- Proceedings of the ninth international conference on Information and knowledge management
- Publisher
- ACM
- Published
- 2000-11-06
- Pages
- 86-93
- OpenAlex
- W2037603696
- Cited by
- 1,005
- References
- 2
- FWCI
- 12.96
- Type
- conference paper
- Language
- en
- Open access
- bronze
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