Efficient active learning of halfspaces via query synthesis

Proceedings of the National Conference on Artificial Intelligence

Volumn 4, Issue , 2015, Pages 2483-2489

  Alabdulmohsin, Ibrahim ^a   Gao, Xin ^a   Zhang, Xiangliang ^a  

^a KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Saudi Arabia)

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; CONVEX OPTIMIZATION; GEOMETRY; ITERATIVE METHODS; LEARNING SYSTEMS; OPTIMIZATION; PRINCIPAL COMPONENT ANALYSIS; QUERY PROCESSING; TEXT PROCESSING;

CONVEX OPTIMIZATION PROBLEMS; FUNDAMENTAL BRANCHES; PRINCIPAL COMPONENTS; REPRESENTATIVE SAMPLING; SENSITIVE INFORMATIONS; TEXT CLASSIFICATION; TRADITIONAL APPROACHES; UNCERTAINTY SAMPLINGS;

ARTIFICIAL INTELLIGENCE;

EID: 84960145136     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (31)

1. Abe, N., and Mamitsuka, H. 1998. Query learning strategies using boosting and bagging. In ICML.
2. Angluin, D. 2001. Queries revisited. In Algorithmic Learning Theory, 12-31. Springer.
3. Balean, M. F., and Long, P. M. 2013. Active and passive learning of linear separators under log-concave distributions. In COLT.
4. Balean, M.-F.; Broder, A.; and Zhang, T. 2007. Margin based active learning. 35-50.
5. Brinker, K. 2003. Incorporating diversity in active learning with support vector machines. In ICML, 59-66.
6. Cohn, D.; Atlas, L.; and Ladner, R. 1994. Improving generalization with active learning. Machine learning 15(2):201-221.
7. Cortes, C., and Vapnik, V. 1995. Support-vector networks. Machine learning 20(3):273-297.
8. CVX Research, I. 2012. CVX: Matlab software for disciplined convex programming, version 2.0. http://cvxr.com/cvx.
9. Dasgupta, S.; Kalai, A. T.; and Monteleoni, C. 2009. Analysis of perceptron-based active learning. JMLR 10:281-299.
10. Dasgupta, S. 2005a. Analysis of a greedy active learning strategy. NIPS 17:337-344.
11. Dasgupta, S. 2005b. Coarse sample complexity bounds for active learning. In NIPS.
12. Fan, R.-E.; Chang, K.-W.; Hsieh, C.-J.; Wang, X.-R.; and Lin, C.-J. 2008. LIBLINEAR: A library for large linear classification. JMLR 9:1871-1874.
13. Freund, Y.; Seung, H. S.; Shamir, E.; and Tishby, N. 1997. Selective sampling using the query by committee algorithm. Machine learning 28(2-3):133-168.
14. Haussier, D., and Warmuth, M. 1993. The probably approximately correct (PAC) and other learning models. 291-312.
15. King, R. D., et al. 2009. The automation of science. Science 324(5923):85-89.
16. Lewis, D. D., and Gale, W. A. 1994. A sequential algorithm for training text classifiers. In Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, 3-12. Springer-Verlag New York, Inc.
17. Liu, Y. 2004. Active learning with support vector machine applied to gene expression data for cancer classification. Journal of chemical information and computer sciences 44(6):1936-1941.
18. Lobo, M. S.; Vandenberghe, L.; Boyd, S.; and Lebret, H. 1998. Applications of second-order cone programming. Linear algebra and its applications 284(1):193-228.
19. McCallum, A., and Nigam, K. 1998. Employing EM and pool-based active learning for text classication. In ICML.
20. Mitchell, T. M. 1982. Generalization as search. Artificial intelligence 18(2):203-226.
21. Nelson, B.; Rubinstein, B. I.; Huang, L.; Joseph, A. D.; Lee, S. J.; Rao, S.; and Tygar, J. 2012. Query strategies for evading convex-inducing classifiers. JMLR 13:1293-1332.
22. Roy, N., and McCallum, A. 2001. Toward optimal active learning through monte carlo estimation of error reduction. In ICML.
23. Schein, A. I., and Ungar, L. H. 2007. Active learning for logistic regression: an evaluation. Machine Learning 68(3):235-265.
24. Sculley, D. 2007. Online active learning methods for fast label-efficient spam filtering. In CEAS.
25. Settles, B. 2010. Active learning literature survey. Computer Science Technical Report 1648, University of Wisconsin-Madison.
26. Settles, B. 2011. From theories to queries: Active learning in practice. Challenges in Machine Learning 6:1-18.
27. Tong, S., and Koller, D. 2002. Support vector machine active learning with applications to text classification. JMLR 2:45-66.
28. Valiant, L. G. 1984. A theory of the learnable. Communications of the ACM 27(11):1134-1142.
29. Vandenberghe, L.; Boyd, S.; and Wu, S.-P. 1998. Determinant maximization with linear matrix inequality constraints. SIAM journal on matrix analysis and applications 19(2):499-533.
30. Xu, Z.; Yu, K.; Tresp, V.; Xu, X.; and Wang, J. 2003. Representative sampling for text classification using support vector machines. In Advances in Information Retrieval, 393-407. Springer.
31. Yuan, G.-X.; Ho, C.-H.; and Lin, C.-J. 2012. Recent advances of large-scale linear classification. Proceedings of the IEEE 100(9):2584-2603.