Classifier learning with hidden information

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 07-12-June-2015, Issue , 2015, Pages 4969-4977

  Wang, Ziheng ^a   Ji, Qiang ^a  

^a RENSSELAER POLYTECHNIC INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; PATTERN RECOGNITION;

CLASSIFIER LEARNING; DATA DRIVEN; HIDDEN INFORMATION; TRAINING DATA;

CLASSIFICATION (OF INFORMATION);

EID: 84959238952     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7299131     Document Type: Conference Paper

References (25)

1. Vladimir Vapnik and Akshay Vashist. A new learning paradigm: Learning using privileged information. Neural Networks, 22:544-557, July 2009.
2. Jixu Chen, Xiaoming Liu, and Siwei Lyu. Boosting with side information. In ACCV, 2012.
3. Viktoriia Sharmanska, Novi Quadrianto, and Christoph H Lampert. Learning to rank using privileged information. In ICCV, 2013.
4. Ziheng Wang, Xiaoyang Wang, and Qiang Ji. Learning with hidden information. In ICPR, pages 238-243. IEEE, 2014.
5. ZihengWang, Tian Gao, and Qiang Ji. Learning with hidden information using a max-margin latent variable model. In ICPR, pages 1389-1394. IEEE, 2014.
6. Zuoguan Wang, Gerwin Schalk, and Qiang Ji. Anatomically constrained decoding of finger flexion from electrocorticographic signals. In Advances in Neural Information Process-ing Systems, pages 2070-2078, 2011.
7. Jan Feyereisl and Uwe Aickelin. Privileged information for data clustering. Information Sciences, 194:4-23, 2012.
8. Shereen Fouad, Peter Tino, Somak Raychaudhury, and Petra Schneider. Learning using privileged information in prototype based models. In ICANN. 2012.
9. Dmitry Pechyony and Vladimir Vapnik. On the Theory of Learning with Privileged Information. In NIPS, 2010.
10. Lingfeng Niu, Yong Shi, and Jianmin Wu. Learning using privileged information with l-1 support vector machine. In WI-IAT, 2012.
11. Jun Liua,Wenxin Zhua, and Ping Zhonga. A new multi-class support vector algorithm based on privileged information. Journal of Information and Computational Science, 2013.
12. You Ji, Shiliang Sun, and Yue Lu. Multitask multiclass privileged information support vector machines. In ICPR, 2012.
13. Maksim Lapin, Matthias Hein, and Bernt Schiele. Learning using privileged information: Svm+ and weighted SVM. arXiv preprint arXiv:1306. 3161, 2013.
14. Feng Cai and Vladimir Cherkassky. Generalized smo algorithm for SVM-based multitask learning. Neural Networks and Learning Systems, IEEE Transactions on, 23(6):997-1003, 2012.
15. Vikas Sindhwani, Partha Niyogi, and Mikhail Belkin. A co-regularization approach to semi-supervised learning with multiple views. In ICML Workshop, 2005.
16. Jason Farquhar, David Hardoon, Hongying Meng, John S Shawe-taylor, and Sandor Szedmak. Two view learning: Svm-2k, theory and practice. In NIPS, 2005.
17. David S Rosenberg and Peter L Bartlett. The rademacher complexity of co-regularized kernel classes. In International Conference on Artificial Intelligence and Statistics, 2007.
18. Theodoros Evgeniou and Massimiliano Pontil. Regularized multi-task learning. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 109-117. ACM, 2004.
19. Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama, and Kiyoshi Asai. Multi-task learning via conic programming. In NIPS, 2007.
20. Ya Xue, Xuejun Liao, Lawrence Carin, and Balaji Krishnapuram. Multi-task learning for classification with dirichlet process priors. The Journal of Machine Learning Research, 8:35-63, 2007.
21. Laurent Jacob, Francis Bach, Jean-Philippe Vert, et al. Clustered multi-task learning: A convex formulation. In NIPS, volume 21, pages 745-752, 2008.
22. Yu Zhang and Dit-Yan Yeung. A convex formulation for learning task relationships in multi-task learning. In 26th Conference on Uncertainty in Artificial Intelligence (UAI), pages 733-742, 2010.
23. P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews. The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression. In CVPR Workshop, pages 94-101, june 2010.
24. C. Lampert, H. Nickisch, and S. Harmeling. Attribute-based classification for zero-shot learning of object categories. In PAMI, 2013.
25. Herbert Bay, Tinne Tuytelaars, and Luc Van Gool. Surf: Speeded up robust features. In ECCV. 2006.