Beyond banditron: A conservative and efficient reduction for online multiclass prediction with bandit setting model

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2009, Pages 71-80

  Chen, Guangyun ^a   Chen, Gang ^a   Zhang, Jianwen ^a   Chen, Shuo ^a   Zhang, Changshui ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Bandit setting model; One versus all reduction; Online multiclass prediction; Passive aggressive algorithm

Indexed keywords

BANDIT SETTING MODEL; MULTICLASS PREDICTION; ONE VERSUS ALL REDUCTION; PASSIVE-AGGRESSIVE ALGORITHM;

FORECASTING; LABELS;

LEARNING ALGORITHMS;

EID: 77951171863     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2009.36     Document Type: Conference Paper

References (18)

1. S. Kakade, S. Shalev-Shwartz, and A. Tewari, "Efficient bandit algorithms for online multiclass prediction," in Proceedings of the 25th international conference on Machine learning. ACM New York, NY, USA, 2008, pp. 440-447.
2. A. Beygelzimer, J. Langford, and T. Zhang, "The Offset Tree for Learning with Partial Labels," Arxiv preprint arXiv:0812.4044, 2008.
3. V. Vapnik, The nature of statistical learning theory. springer, 2000.
4. B. Zadrozny, J. Langford, and N. Abe, "Cost-sensitive learning by cost-proportionate example weighting," in Proceedings of the Third IEEE International Conference on Data Mining, vol. 2003. Citeseer, 2003, pp. 435-442.
5. P. Auer, N. Cesa-Bianchi, Y. Freund, and R. Schapire, "Gambling in a rigged casino: The adversarial multi-armed bandit problem," in ANNUAL SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE, vol. 36. IEEE Computer Society Press, 1995, pp. 322-331.
6. J. Langford and T. Zhang, "The epoch-greedy algorithm for contextual multi-armed bandits," Advances in Neural Information Processing Systems, 2007.
7. E. Allwein, R. Schapire, and Y. Singer, "Reducing multiclass to binary: A unifying approach for margin classifiers," The Journal of Machine Learning Research, vol. 1, pp. 113-141, 2001.
8. K. Crammer, O. Dekel, J. Keshet, S. Shalev-Shwartz, and Y. Singer, "Online passive-aggressive algorithms," The Journal of Machine Learning Research, vol. 7, pp. 551-585, 2006.
9. R. Duda, P. Hart, and D. Stork, Pattern classification. Wiley New York, 2001.
10. T. Hastie and R. Tibshirani, "Classification by pairwise coupling," Annals of Statistics, pp. 451-471, 1998.
11. K. Crammer and Y. Singer, "Ultraconservative online algorithms for multiclass problems," The Journal of Machine Learning Research, vol. 3, pp. 951-991, 2003.
12. S. Boyd and L. Vandenberghe, Convex optimization. Cambridge university press, 2004.
13. F. Rosenblatt, "The perceptron: A probabilistic model for information storage and organization in the brain," Psychological review, vol. 65, no. 6, pp. 386-408, 1958.
14. D. Lewis, Y. Yang, T. Rose, and F. Li, "Rcv1: A new benchmark collection for text categorization research," The Journal of Machine Learning Research, vol. 5, pp. 361-397, 2004.
15. X. Zhu, "Semi-supervised learning literature survey," Computer Science, University of Wisconsin-Madison, 2006.
16. D. Zhou, O. Bousquet, T. Lal, J. Weston, and B. Scholkopf, "Learning with local and global consistency," in Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference. The MIT Press, 2004, p. 321.
17. F. Wang and C. Zhang, "Label propagation through linear neighborhoods," IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 1, pp. 55-67, 2008.
18. M. Fink, S. Shalev-Shwartz, Y. Singer, and S. Ullman, "Online multiclass learning by interclass hypothesis sharing," in Proceedings of the 23rd international conference on Machine learning. ACM New York, NY, USA, 2006, pp. 313-320.