Regularized multi-class semi-supervised boosting

2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009

Volumn , Issue , 2009, Pages 967-974

  Saffari, Amir ^a   Leistner, Christian ^a   Bischof, Horst ^a  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; LEARNING ALGORITHMS; LEARNING SYSTEMS;

BINARY CLASSIFICATION; LARGE-SCALE PROBLEM; MULTI-CLASS MARGINS; MULTI-CLASS PROBLEMS; OBJECT CATEGORIZATION; STANDARD MACHINES; UNLABELED SAMPLES; VERY LARGE DATUM;

SEMI-SUPERVISED LEARNING;

EID: 70450219022     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2009.5206715     Document Type: Conference Paper

References (29)

1. E. L. Allwein, R. E. Schapire, and Y. Singer. Reducing multiclass to binary: A unifying ap-proach for margin classifiers. JMLR, 1: 113- 141, 2000.
2. A. Azran. The rendezvous algorithm: multiclass semi-supervised learning with markov random walks. In ICML, pages 49-56, 2007.
3. M. Belkin, P. Niyogi, and V. Sindhwani. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. JMLR, 7: 2399-2434, 2006.
4. A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In COLT, pages 92-100, 1998.
5. L. Breiman. Random forests. Machine Learning, 45(1): 5-32, 2001.
6. B. Cestnik. Estimating probabilities: A crucial task in machine learning. In ECAI, pages 147- 149, 1990.
7. C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines, 2001.
8. O. Chapelle, J. Weston, and B. Schölkopf. Cluster kernels for semi-supervised learning. In NIPS, pages 585-592, 2003.
9. M. Everingham, A. Zisserman, C. K. I. Williams, and L. Van Gool. The pascal visual object classes challenge 2006 results. Technical report, 2006.
10. J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: A statistical view of boosting. The Annals of Statistics, 38(2): 337- 374, 2000.
11. P. Geurts, D. Ernst, and L. Wehenkel. Extremely randomized trees. In Machine Learning, volume 63, pages 3-42, 2006.
12. T. Joachims. Transductive inference for text classification using support vector machines. In ICML, pages 200-209, 1999.
13. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR, pages 2169-2178, 2006.
14. C. Leistner, H. Grabner, and H. Bischof. Semi-supervised boosting using visual similarity learning. In CVPR, 2008.
15. J. Li and J. Fine. ROC analysis with multiple classes and multiple tests: Methodology and its application in microarray studies. Biostatistics, 9(3): 566-576, 2008.
16. Y. Lin. A note on margin-based loss functions in classification. Statistics & Probability Letters, 68(1): 73-82, 2004.
17. G. S. Mann and A. Mccallum. Simple, robust, scalable semi-supervised learning via expectation regularization. In ICML, pages 593-600, 2007.
18. L. Mason, J. Baxter, P. Bartlett, and M. Frean. Functional gradient techniques for combining hypotheses, pages 221-247. MIT Press, Cambridge, MA., 1999.
19. A. Mccallum, G. Mann, and G. Druck. Generalized expectation criteria. Technical report, University of Massachusetts Amherst, August 2007.
20. S. Rogers and M. Girolami. Multi-class semisupervised learning with the e-truncated multinomial probit gaussian process. JMLR, 1: 17- 32, 2007.
21. S. Rosset, J. Zhu, T. Hastie, and R. Schapire. Boosting as a regularized path to a maximum margin classifier. JMLR, 5: 941-973, 2004.
22. A. Saffari, H. Grabner, and H. Bischof. SERBoost: Semi-supervised boosting with expectation regularization. In ECCV, 2008.
23. R. Schapire, M. Rochery, M. Rahim, and N. Gupta. Incorporating prior knowledge into boosting. In ICML, 2002.
24. V. Sindhwani and S. S. Keerthi. Large scale semi-supervised linear svms. In SIGIR, pages 477-484, 2006.
25. Y. Song, C. Zhang, and J. Lee. Graph based multi-class semi-supervised learning using gaussian process. In IAPR workshop on Structural, Syntactic, and Statistical Pattern Recognition, pages 450-458, 2006.
26. A. Torralba, K. P. Murphy, and W. T. Freeman. Sharing visual features for multiclass and multiview object detection. PAMI, 29(5): 854-869, 2007.
27. L. Xu and D. Schuurmans. Unsupervised and semi-supervised multi-class support vector machines. In AAAI, 2005.
28. X. Zhu and Z. Ghahramani. Learning from labeled and unlabeled data with label propagation. Technical report, 2002.
29. H. Zou, J. Zhu, and T. Hastie. New multicategory boosting algorithms based on multicategory fisher-consistent losses. Annals of Applied Statistics, 2008.