Learning with hidden information using a max-margin latent variable model

Proceedings - International Conference on Pattern Recognition

Volumn , Issue , 2014, Pages 1389-1394

  Wang, Ziheng ^a   Gao, Tian ^a   Ji, Qiang ^a  

^a RENSSELAER POLYTECHNIC INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GESTURE RECOGNITION;

CLASSIFIER LEARNING; DIGIT RECOGNITION; HIDDEN INFORMATION; LATENT VARIABLE MODELING; PRIOR KNOWLEDGE; TRAINING DATA;

CLASSIFICATION (OF INFORMATION);

EID: 84919881476     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.2014.248     Document Type: Conference Paper

References (24)

1. T. Antonio, "Contextual priming for object detection," Int. J. Comput. Vision, vol. 53, pp. 169-191, 2003.
2. M. Marszalek, I. Laptev, and C. Schmid, "Actions in context," in Computer Vision and Pattern Recognition, IEEE Conference on, 2009, pp. 2929-2936.
3. X. Wang and Q. Ji, "Incorporating contextual knowledge to dynamic bayesian networks for event recognition," in International Conference on Pattern Recognition, 2012.
4. A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth, "Describing objects by their attributes," in Computer Vision and Pattern Recognition, IEEE Conference on, June 2009, pp. 1778-1785.
5. Y. Wang and G. Mori, "A discriminative latent model of object classes and attributes," in ECCV, 2010, pp. 155-168.
6. J. Wang, Z. Liu, Y. Wu, and J. Yuan, "Mining actionlet ensemble for action recognition with depth cameras," in Computer Vision and Pattern Recognition, IEEE Conference on, 2012.
7. A. Quattoni, S. Wang, L. P. Morency, M. Collins, and T. Darrell, "Hidden-state conditional random fields," in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007.
8. V. Vapnik and A. Vashist, "A new learning paradigm: Learning using privileged information," Neural Networks, vol. 22, pp. 544-557, July 2009.
9. V. Vapnik, A. Vashist, and N. Pavlovitch, "Learning using hidden information (learning with teacher)," in Neural Networks, 2009. IJCNN 2009. International Joint Conference on. IEEE, 2009, pp. 3188-3195.
10. D. Pechyony and V. Vapnik, "On the Theory of Learning with Privileged Information," in Advances in Neural Information Processing Systems 23, 2010.
11. D. Pechyony, R. Izmailov, A. Vashist, and V. Vapnik, "Smo-style algorithms for learning using privileged information," in DMIN'10, 2010, pp. 235-241.
12. D. Pechyony and V. Vapnik, "Fast optimization algorithms for solving svm," Statistical Learning and Data Science. Chapman &Hall, 2011.
13. L. Niu and J. Wu, "Nonlinear l-1 support vector machines for learning using privileged information," in Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on. IEEE, 2012, pp. 495-499.
14. V. Sharmanska, I. Austria, N. Quadrianto, and C. H. Lampert, "Learning to rank using privileged information," in ICCV, 2013.
15. M. Lapin, M. Hein, and B. Schiele, "Learning using privileged information: Svm+ and weighted svm," arXiv preprint arXiv: 1306.3161, 2013.
16. L. Liang and V. Cherkassky, "Connection between svm+ and multitask learning," in Neural Networks, 2008. IJCNN 2008.(IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on. IEEE, 2008, pp. 2048-2054.
17. J. Chen, X. Liu, and S. Lyu, "Boosting with side information," in Proceeding of 11th Asian Conference on Computer Vision, Nov 2012.
18. H. Yang and I. Patras, "Privileged information-based conditional regression forest for facial feature detection," in Automatic Face and Gesture Recognition, 10th IEEE International Conference and Workshops on, 2013.
19. H. Wang, F. Nie, H. Huang, S. Risacher, A. J. Saykin, and L. Shen, "Identifying ad-sensitive and cognition-relevant imaging biomarkers via joint classification and regression," in MICCAI'11, 2011, pp. 115-123.
20. J. Feyereisl and U. Aickelin, "Privileged information for data clustering," Information Sciences, vol. 194, pp. 4-23, 2012.
21. S. Fouad, P. Tino, S. Raychaudhury, and P. Schneider, "Learning using privileged information in prototype based models," in Artificial Neural Networks and Machine Learning-ICANN 2012. Springer, 2012, pp. 322-329.
22. C.-N. J. Yu and T. Joachims, "Learning structural svms with latent variables," in Proceedings of the 26th Annual International Conference on Machine Learning, 2009, pp. 1169-1176.
23. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, 1998.
24. ChaLearn, "ChaLearn gesture dataset (CGD2011)," California, 2011