SemiBoost: Boosting for semi-supervised learning

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 31, Issue 11, 2009, Pages 2000-2014

  Mallapragada, Pavan Kumar ^a   Jin, Rong ^a   Jain, Anil K ^a   Liu, Yi ^a  

^a Michigan State University   (United States)

Author keywords

Boosting; Cluster assumption; Machine learning; Manifold assumption; Semi supervised improvement; Semi supervised learning

Indexed keywords

BOOSTING; CLUSTER ASSUMPTION; MACHINE LEARNING; MANIFOLD ASSUMPTION; SEMI-SUPERVISED IMPROVEMENT; SEMI-SUPERVISED LEARNING;

CLUSTERING ALGORITHMS; COMPUTATIONAL EFFICIENCY; EDUCATION; PATTERN RECOGNITION; ROBOT LEARNING; SUPERVISED LEARNING; TEXT PROCESSING;

LEARNING ALGORITHMS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; DECISION SUPPORT SYSTEM; METHODOLOGY; THEORETICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DECISION SUPPORT TECHNIQUES; MODELS, THEORETICAL; PATTERN RECOGNITION, AUTOMATED;

EID: 70349871686     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2008.235     Document Type: Article

References (35)

1. H.J. Scudder, "Probability of Error of Some Adaptive Pattern-Recognition Machines," IEEE Trans. Information Theory, vol. 11, no. 3, pp. 363-371, July 1965.
2. H. Robbins and S. Monro, "A Stochastic Approximation Method," Annals of Math. Statistics, vol. 22, pp. 400-407, 1951.
3. X. Zhu and Z. Ghahramani, "Learning from Labeled and Unlabeled Data with Label Propagation," Technical Report CMU-CALD-02-107, Carnegie Mellon Univ., 2002.
4. Y. Bengio, O.B. Alleau, and N. Le Roux, "Label Propagation and Quadratic Criterion," Semi-Supervised Learning, O. Chapelle, B. Schölkopf, and A. Zien, eds., pp. 193-216, MIT Press, 2006.
5. M. Szummer and T. Jaakkola, "Partially Labeled Classification with Markov Random Walks," Proc. Neural Information Processing Systems Conf., pp. 945-952, 2001.
6. A. Blum and S. Chawla, "Learning from Labeled and Unlabeled Data Using Graph Mincuts," Proc. 18th Int'l Conf. Machine Learning, pp. 19-26, 2001.
7. T. Joachims, "Transductive Learning via Spectral Graph Partitioning," Proc. 20th Int'l Conf. Machine Learning, pp. 290-297, 2003.
8. O. Chapelle and A. Zien, "Semi-Supervised Classification by Low Density Separation," Proc. 10th Int'l Workshop Artificial Intelligence and Statistics, pp. 57-64, 2005.
9. Semi-Supervised Learning, O. Chapelle, B. Scholkopf, and A. Zien, eds. MIT Press, 2006.
10. T. Joachims, "Transductive Inference for Text Classification Using Support Vector Machines," Proc. 16th Int'l Conf. Machine Learning pp. 200-209, 1999.
11. G. Fung and O. Mangasarian, "Semi-Supervised Support Vector Machines for Unlabeled Data Classification," Optimization Methods and Software, vol. 15, pp. 29-44, 2001.
12. M. Belkin, P. Niyogi, and V. Sindhwani, "Manifold Regularization: A Geometric Framework for Learning from Examples," Technical Report TR-2004-06, Dept. of Computer Science, Univ. of Chicago, 2004.
13. V. Vural, G. Fung, J.G. Dy, and B. Rao, "Semi-Supervised Classifiers Using A-Priori Metric Information," Optimization Methods and Software J., special issue on machine learning and data mining, to appear.
14. Y. Freund and R.E. Schapire, "Experiments with a New Boosting Algorithm," Proc. 13th Int'l Conf. Machine Learning, pp. 148-156, 1996.
15. C. Rosenberg, M. Hebert, and H. Schneiderman, "Semi-Supervised Self-Training of Object Detection Models," Proc. Seventh Workshop Applications of Computer Vision, vol. 1, pp. 29-36, Jan. 2005.
16. K.P. Bennett, A. Demiriz, and R. Maclin, "Exploiting Unlabeled Data in Ensemble Methods," Proc. Eighth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 289-296, 2002.
17. F. d'Alche Buc, Y. Grandvalet, and C. Ambroise, "Semi-Supervised Marginboost," Proc. Neural Information Processing Systems Conf., pp. 553-560, 2002.
18. X. Zhu, Z. Ghahramani, and J. Lafferty, "Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions," Proc. 20th Int'l Conf. Machine Learning, pp. 912-919, 2003.
19. O. Chapelle and A. Zien, "Semi-Supervised Classification by Low Density Separation," Proc. 10th Int'l Workshop Artificial Intelligence and Statistics, pp. 57-64, 2005.
20. A. Blum and T. Mitchell, "Combining Labeled and Unlabeled Data with Co-Training," Proc. Workshop Computational Learning Theory, pp. 92-100, 1998.
21. D. Miller and H. Uyar, "A Mixture of Experts Classifier with Learning Based on Both Labeled and Unlabeled Data," Proc. Neural Information Processing Systems Conf., pp. 571-577, 1996.
22. K. Nigam, A.K. McCallum, S. Thrun, and T. Mitchell, "Text Classification from Labeled and Unlabeled Documents Using EM," Machine Learning, vol. 39, pp. 103-134, 2000.
23. N.D. Lawrence and M.I. Jordan, "Semi-Supervised Learning via Gaussian Processes," Proc. Neural Information Processing Systems Conf., pp. 753-760, 2005.
24. K. Bennett and A. Demiriz, "Semi-Supervised Support Vector Machines," Proc. Neural Information Processing Systems Conf., pp. 368-374, 1998.
25. Y. Freund and R.E. Schapire, "A Decision-Theoretic Generalization of Online Learning and an Application to Boosting," J. Computer and System Sciences, vol. 55, pp. 119-139, Aug. 1997.
26. D. Zhou, J. Huang, and B. Scholkopf, "Learning from Labeled and Unlabeled Data on a Directed Graph," Proc. 22nd Int'l Conf. Machine Learning, pp. 1036-1043, 2005.
27. J. Friedman, T. Hastie, and R. Tibshirani, "Special Invited Paper. Additive Logistic Regression: A Statistical View of Boosting," The Annals of Statistics, vol. 28, pp. 337-374, Apr. 2000.
28. P.K. Mallapragada, R. Jin, A.K. Jain, and Y. Liu, "Semiboost: Boosting for Semi-Supervised Learning," Technical Report MSU-CSE-07-197, Michigan State Univ., 2007.
29. L. Mason, J. Baxter, P. Bartlett, and M. Frean, "Boosting Algorithms as Gradient Descent in Function Space," Proc. Neural Information Processing Systems Conf., pp. 512-518, 1999.
30. T. Minka, "Expectation-Maximization As Lower Bound Maximization," tutorial, http://www-white.media.mit.edu/tpminka/papers/em.html, 1998.
31. A. Jain and X. Lu, "Ethnicity Identification from Face Images," Proc. SPIE, Defense and Security Symp., vol. 5404, pp. 114-123, 2004.
32. A.K. Jain and F. Farrokhina, "Unsupervised Texture Segmentation Using Gabor Filters," Pattern Recognition, vol. 24, pp. 1167-1186, 1991.
33. I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, second ed. Morgan Kaufmann, 2005.
34. L. Reyzin and R.E. Schapire, "How Boosting the Margin Can Also Boost Classifier Complexity," Proc. 22nd Int'l Conf. Machine Learning, pp. 753-760, 2006.
35. J. Platt, N. Cristianini, and J. Shawe, "Large Margin DAGs for Multiclass Classification," Proc. Neural Information Processing Systems Conf., pp. 547-553, 2000.