A simple algorithm for semi-supervised learning with improved generalization error bound

Proceedings of the 29th International Conference on Machine Learning, ICML 2012

Volumn 2, Issue , 2012, Pages 1223-1230

  Ji, Ming ^a   Yang, Tianbao ^b   Lin, Binbin ^c   Jin, Rong ^b   Han, Jiawei ^a  

^a University of Illinois at Urbana Champaign   (United States)

^b Michigan State University   (United States)

^c ZHEJIANG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BASIS FUNCTIONS; EMPIRICAL STUDIES; GENERALIZATION ERROR BOUNDS; INTEGRAL OPERATORS; PREDICTION FUNCTION; REGRESSION ERRORS; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING; SIMPLE ALGORITHM; SIMPLE LINEAR REGRESSION;

ALGORITHMS; EIGENVALUES AND EIGENFUNCTIONS; MATHEMATICAL OPERATORS;

SUPERVISED LEARNING;

EID: 84867114245     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. Belkin, Mikhail, Niyogi, Partha, and Sindhwani, Vikas. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7:2399-2434, 2006.
2. Bishop, Christopher M. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, 2006.
3. Candès, Emmanuel J. and Tao, Terence. Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Transactions on Information Theory, 52(12):5406-5425, 2006.
4. Castelli, Vittorio and Cover, Thomas M. On the exponential value of labeled samples. Pattern Recognition Letters, 16(1):105-111, 1995.
5. Castelli, Vittorio and Cover, Thomas M. The relative value of labeled and unlabeled samples in pattern recognition with an unknown mixing parameter. IEEE Transactions on Information Theory, 42(6):2102-2117, 1996.
6. Drucker, Harris, Burges, Christopher J. C., Kaufman, Linda, Smola, Alex J., and Vapnik, Vladimir. Support vector regression machines. In NIPS, pp. 155-161, 1996.
7. Fergus, Rob, Weiss, Yair, and Torralba, Antonio. Semi-supervised learning in gigantic image collections. In NIPS, pp. 522-530, 2009.
8. Frank, A. and Asuncion, A. UCI machine learning repository, 2010. URL http://archive.ics.uci.edu/ml.
9. Guo, Xin and Zhou, Ding-Xuan. An empirical feature-based learning algorithm producing sparse approximations. Applied and Computational Harmonic Analysis, 2011.
10. Koltchinskii, Vladimir. Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems. Springer, 2011.
11. Koltchinskii, Vladimir and Yuan, Ming. Sparsity in multiple kernel learning. Annuals of Statistics, 38:3660-3694, 2010.
12. Lafferty, John D. and Wasserman, Larry A. Statistical analysis of semi-supervised regression. In NIPS, pp. 801-808, 2007.
13. Minh, Ha Quang. Some properties of gaussian reproducing kernel hilbert spaces and their implications for function approximation and learning theory. Constructive Approximation, 32:307-338, 2010.
14. Nadler, Boaz, Srebro, Nathan, and Zhou, Xueyuan. Statistical analysis of semi-supervised learning: The limit of infinite unlabeled data. In NIPS, pp. 1330-1338, 2009.
15. Niyogi, P. Manifold regularization and semi-supervised learning: Some theoretical analyses. Technical report, Computer Science Department, University of Chicago, 2008.
16. Rigollet, Philippe. Generalization error bounds in semi-supervised classification under the cluster assumption. Journal of Machine Learning Research, 8:1369-1392, 2007.
17. Saunders, Craig, Gammerman, Alexander, and Vovk, Volodya. Ridge regression learning algorithm in dual variables. In ICML, pp. 515-521, 1998.
18. Seeger, Matthias. Learning with labeled and unlabeled data. Technical report, 2001.
19. Singh, Aarti, Nowak, Robert D., and Zhu, Xiaojin. Unlabeled data: Now it helps, now it doesn't. In NIPS, pp. 1513-1520, 2008.
20. Sinha, Kaushik and Belkin, Mikhail. Semi-supervised learning using sparse eigenfunction bases. In NIPS, pp. 1687-1695, 2009.
21. Smale, Steve and Zhou, Ding-Xuan. Geometry on probability spaces. Constructive Approximation, 30:311-323, 2009.
22. Steinwart, Ingo, Hush, Don R., and Scovel, Clint. An explicit description of the reproducing kernel hilbert spaces of gaussian rbf kernels. IEEE Transactions on Information Theory, 52(10):4635-4643, 2006.
23. Tsybakov, Alexandre B. Introduction to Nonparametric Estimation. Springer, 1st edition, 2008.
24. Zhang, Tong and Ando, Rie Kubota. Analysis of spectral kernel design based semi-supervised learning. In NIPS, pp. 1601-1608, 2005.
25. Zhu, Xiaojin. Semi-supervised learning literature survey. Technical report, Computer Sciences, University of Wisconsin-Madison, 2008.