Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Semi-Supervised Learning

  • Xiaojin Zhu
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_749

Synonyms

Definition

Semi-supervised learning uses both labeled and unlabeled data to perform an otherwise  supervised learning or  unsupervised learning task.

In the former case, there is a distinction between inductive semi-supervised learning and transductive learning. In inductive semi-supervised learning, the learner has both labeled training data \(\{(\mathbf{x}_i, y_i)\}_{i=1}^l {\mathop{\sim}^{iid}} p(\mathbf{x},y)\)

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Recommended Reading

  1. Abney, S. (2007). Semisupervised learning for computational linguistics. Florida: Chapman & Hall/CRC.CrossRefGoogle Scholar
  2. Balcan, M.-F., & Blum, A. (2009). A discriminative model for semi-supervised learning. Journal of the ACM.Google Scholar
  3. Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7, 2399–2434.MathSciNetGoogle Scholar
  4. Blum, A., & Chawla, S. (2001). Learning from labeled and unlabeled data using graph mincuts. In Proceedings of the 18th international conference on machine learning (pp. 19–26). San Francisco: Morgan Kaufmann.Google Scholar
  5. Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. In COLT: Proceedings of the workshop on computational learning theory (pp. 92–100). New York: ACM.Google Scholar
  6. Castelli, V., & Cover, T. (1995). The exponential value of labeled samples. Pattern Recognition Letters, 16(1), 105–111.CrossRefGoogle Scholar
  7. Chapelle, O., Zien, A., & Schölkopf, B., (Eds.) (2006). Semi-supervised learning. Cambridge, MA MIT Press.Google Scholar
  8. Joachims, T. (1999). Transductive inference for text classification using support vector machines. In Proceedings of the 16th international conference on machine learning (pp. 200–209). San Francisco: Morgan Kaufmann.Google Scholar
  9. Nigam, K., McCallum, A. K., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2/3), 103–134.MATHCrossRefGoogle Scholar
  10. Seeger, M. (2001). Learning with labeled and unlabeled data. Technical report. University of Edinburgh, Edinburgh.Google Scholar
  11. Sindhwani, V., Niyogi, P., & Belkin, M. (2005). A co-regularized approach to semi-supervised learning with multiple views. In Proceedings of the 22nd ICML workshop on learning with multiple views.Google Scholar
  12. Vapnik, V. (1998). Statistical learning theory. New York: Wiley.MATHGoogle Scholar
  13. Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the 33rd annual meeting of the association for computational linguistics (pp. 189–196).CrossRefGoogle Scholar
  14. Zhu, X., Ghahramani, Z., & Lafferty, J. (2003). Semi-supervised learning using Gaussian fields and harmonic functions. In The 20th international conference on machine learning (ICML).Google Scholar
  15. Zhu, X., & Goldberg, A. B. (2009). Synthesis lectures on artificial intelligence and machine learning. In Introduction to semi-supervised learning. Morgan & Claypool.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Xiaojin Zhu

There are no affiliations available