Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields

NIPS 2006: Proceedings of the 19th International Conference on Neural Information Processing Systems

Volumn , Issue , 2006, Pages 793-800

  Lee, Chi Hoon ^a   Jiao, Feng ^b   Wang, Shaojun ^c   Schuurmans, Dale ^a   Greiner, Russell ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

^b UNIVERSITY OF WATERLOO   (Canada)

^c Wright State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONDITIONAL ENTROPY; DATA DEPENDENT; DISCRIMINATIVE RANDOM FIELDS; IMAGES PROCESSING; LABELED DATA; LOG LIKELIHOOD; MAP ESTIMATION; SEMI-SUPERVISED; SPATIAL DEPENDENCIES; TRAINING DATA;

IMAGE ENHANCEMENT;

EID: 85157973211     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Y. Altun, D. McAllester, and M. Belkin. Maximum margin semi-supervised learning for structured variables. In NIPS 18. 2006.
2. J. Besag. On the statistical analysis of dirty pictures. Journal of Royal Statistical Society. Series B, 48:3:259-302, 1986.
3. A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In COLT, 1998.
4. Yuri Boykov, Olga Veksler, and Ramin Zabih. Fast approximate energy minimization via graph cuts. In ICCV (1), pages 377-384, 1999.
5. G. Celeux and G. Govaert. A classification EM algorithm for clustering and two stochastic versions. Comput. Stat. Data Anal., 14(3):315-332, 1992.
6. A. Corduneanu and T. Jaakkola. Data dependent regularization. In O. Chapelle, B. Schoelkopf, and A. Zien, editors, Semi-Supervised Learning. MIT Press, 2006.
7. C. Garcia and J.A. Moreno. Kernel based method for segmentation and modeling of magnetic resonance images. LNCS, 3315:636-645, Oct 2004.
8. Y. Grandvalet and Y. Bengio. Semi-supervised learning by entropy minimization. In NIPS 17, 2004.
9. F. Jiao, S. Wang, C. Lee, R. Greiner, and D Schuurmans. Semi-supervised conditional random fields for improved sequence segmentation and labeling. In COLING/ACL, 2006.
10. S. Kumar and M. Hebert. Discriminative fields for modeling spatial dependencies in natural images. In NIPS 16, 2003.
11. S. Kumar and M. Hebert. Discriminative random fields: A discriminative framework for contextual interaction in classification. In CVPR, 2003.
12. J. Lafferty, F. Pereira, and A. McCallum. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML, 2001.
13. C. Lee, R. Greiner, and O. Zaïane. Efficient spatial classification using decoupled conditional random fields. In 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, pages 272-283, 2006.
14. K. Nigam, A. McCallum, S. Thrun, and T. Mitchell. Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2/3):103-134, 2000.
15. A. Quattoni, M. Collins, and T. Darrell. Conditional random fields for object recognition. In NIPS 17, 2004.
16. S. Roberts, R. Everson, and I. Rezek. Maximum certainty data partitioning, 2000.
17. A. Torralba, K. Murphy, and W. Freeman. Contextual models for object detection using boosted random fields. In NIPS 17, 2004.
18. V. Vapnik. Statistical Learning Theory. John-Wiley, 1998.
19. S.V.N. Vishwanathan, N. Schraudolph, M. Schmidt, and K. Murphy. Accelerated training of conditional random fields with stochastic gradient methods. In ICML, 2006.
20. J. Yedidia, W. Freeman, and Y. Weiss. Generalized belief propagation. In NIPS 13, pages 689-695, 2000.
21. J. Zhang, K. Ma, M.H. Er, and V. Chong. Tumor segmentation from magnetic resonance imaging by learning via one-class support vector machine. Intl. Workshop on Advanced Image Technology, 2004.
22. D. Zhou, O. Bousquet, T. Navin Lal, J. Weston, and B. Schölkopf. Learning with local and global consistency. In NIPS 16, 2004.
23. D. Zhou, J. Huang, and B. Schölkopf. Learning from labeled and unlabeled data on a directed graph. In ICML, 2005.
24. X. Zhu, Z. Ghahramani, and J. Lafferty. Semi-supervised learning using gaussian fields and harmonic functions. In ICML, 2003.