Sparse Bayesian adversarial learning using relevance vector machine ensembles

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2012, Pages 1206-1211

  Zhou, Yan ^a   Kantarcioglu, Murat ^a   Thuraisingham, Bhavani ^a  

^a The University of Texas at Dallas   (United States)

Author keywords

Adversarial learning; Kernel parameter learning; Relevance vector machine; Spare Bayesian learning

Indexed keywords

ADVERSARIAL LEARNING; BAYESIAN LEARNING; DATA DISTRIBUTION; DATA MINING TASKS; KERNEL PARAMETER; MODEL POTENTIAL; MODEL TRAINING; POSITIVE DATA; RELEVANCE VECTOR MACHINE; ROBUST LEARNING; SPARSE BAYESIAN;

DATA MINING; FACE RECOGNITION;

VECTORS;

EID: 84874026375     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2012.58     Document Type: Conference Paper

References (17)

1. M. Kearns and M. Li, "Learning in the presence of malicious errors," SIAM J. of Computing, vol. 22, pp. 807-837, 1993.
2. P. Auer and N. Cesa-Bianchi, "On-line learning with malicious noise and the closure algorithm," Annals of Mathematics and Artificial Intelligence, vol. 23, no. 1-2, pp. 83-99, 1998.
3. M. E. Tipping, "Sparse bayesian learning and the relevance vector machine," J. Mach. Learn. Res., vol. 1, pp. 211-244, Sep. 2001.
4. N. H. Bshouty, N. Eiron, and E. Kushilevitz, "Pac learning with nasty noise," Theoretical Computer Science, vol. 288, p. 2002, 1999.
5. D. Lowd and C. Meek, "Adversarial learning," in Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, 2005, pp. 641-647.
6. N. Dalvi, P. Domingos, Mausam, S. Sanghai, and D. Verma, "Adversarial classification," in Proceedings of the tenth ACM SIGKDD. ACM, 2004, pp. 99-108.
7. M. Kantarcioglu, B. Xi, and C. Clifton, "Classifier evaluation and attribute selection against active adversaries," Data Min. Knowl. Discov., vol. 22, pp. 291-335, January 2011.
8. M. Bruckner and T. Scheffer, "Nash equilibria of static prediction games," in Advances in Neural Information Processing Systems. MIT Press, 2009.
9. W. Liu and S. Chawla, "Mining adversarial patterns via regularized loss minimization," Mach. Learn., vol. 81, pp. 69-83, October 2010.
10. M. Bruckner and T. Scheffer, "Stackelberg games for adversarial prediction problems," in Proceedings of the 17th ACM SIGKDD. ACM, 2011.
11. G. R. G. Lanckriet, L. E. Ghaoui, C. Bhattacharyya, and J. M. I., "A robust minimax approach to classification," Journal of Machine Learning Research, vol. 3, pp. 555-582, 2002.
12. C. H. Teo, A. Globerson, S. T. Roweis, and A. J. Smola, "Convex learning with invariances," in Advances in Neural Information Processing Systems, 2007.
13. O. Dekel and O. Shamir, "Learning to classify with missing and corrupted features," in Proceedings of the International Conference on Machine Learning. ACM, 2008, pp. 216-223.
14. O. Dekel, O. Shamir, and L. Xiao, "Learning to classify with missing and corrupted features," Machine Learning, vol. 81(2), pp. 149-178, 2010.
15. A. Globerson and S. Roweis, "Nightmare at test time: robust learning by feature deletion," in Proceedings of the 23rd ICML. ACM, 2006, pp. 353-360.
16. L. El Ghaoui, G. R. G. Lanckriet, and G. Natsoulis, "Robust classification with interval data," EECS Department, UC Berkeley, Tech. Rep. UCB/CSD-03-1279, Oct 2003.
17. Y. Zhou, M. Kantarcioglu, B. Thuraisingham, and B. Xi, "Adversarial support vector machine learning," in Proceedings of the 18th ACM SIGKDD. ACM, 2012, pp. 1059-1067.