D. Angluin, Queries and concept learning, Machine Learning, vol.27, issue.4, pp.319-342, 1988.
DOI : 10.1007/BF00116828

M. Balcan, A. Broder, and T. Zhang, Margin Based Active Learning, Proc. of the 20 th Conference on Learning Theory, 2007.
DOI : 10.1007/978-3-540-72927-3_5

N. Cesa-bianchi, A. Conconi, and C. Gentile, Learning probabilistic linearthreshold classifiers via selective sampling, Proc. 16th COLT, pp.373-386, 2003.

D. Cohn, L. Atlas, and R. Ladner, Improving generalization with active learning, Machine Learning, vol.27, issue.4, pp.201-221, 1994.
DOI : 10.1007/BF00993277

D. Cohn, Z. Ghahramani, and M. Jordan, Active Learning with Statistical Models, Journal of Artificial Intelligence Research, vol.4, pp.129-145, 1996.

S. Dasgupta, Analysis of a greedy active learning strategy, Advances in Neural Information Processing Systems 17, pp.337-344, 2005.

S. Dasgupta, Coarse sample complexity bounds for active learning, Advances in Neural Information Processing Systems 18, pp.235-242, 2006.

S. Dasgupta, A. T. Kalai, and C. Monteleoni, Analysis of Perceptron-Based Active Learning, In In COLT, pp.249-263, 2005.
DOI : 10.1007/11503415_17

L. Devroye, L. Györfi, and G. Lugosi, A probabilistic Theory of Pattern Recognition, 1997.
DOI : 10.1007/978-1-4612-0711-5

Y. Freund, H. S. Seung, E. Shamir, and N. Tishby, Selective sampling using the query by committee algorithm, Machine Learning, vol.28, issue.2/3, pp.133-168, 1997.
DOI : 10.1023/A:1007330508534

Y. Guo and D. Schuurmans, Discriminative batch mode active learning, Advances in Neural Information Processing Systems (NIPS), pp.593-600, 2008.

S. C. Hoi, R. Jin, J. Zhu, and M. R. Lyu, Batch mode active learning and its application to medical image classification, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.417-424, 2006.
DOI : 10.1145/1143844.1143897

S. C. Hoi, R. Jin, J. Zhu, and M. R. Lyu, Semisupervised SVM batch mode active learning with applications to image retrieval, ACM Transactions on Information Systems, vol.27, issue.3, pp.1-29, 2009.
DOI : 10.1145/1508850.1508854

V. S. Iyengar, C. Apte, and T. Zhang, Active learning using adaptive resampling, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '00, pp.91-98, 2000.
DOI : 10.1145/347090.347110

S. R. Kulkarni, S. K. Mitter, and J. N. Tsitsiklis, Active learning using arbitrary binary valued queries, Machine Learning, vol.11, issue.1, pp.23-35, 1993.
DOI : 10.1023/A:1022627018023

M. Lindenbaum, S. Markovitch, and D. Rusakov, Selective Sampling for Nearest Neighbor Classifiers, Machine Learning, pp.125-152, 2004.
DOI : 10.1023/B:MACH.0000011805.60520.fe

T. M. Mitchell, Generalization as search, Artificial Intelligence, vol.18, issue.2, pp.203-226, 1982.
DOI : 10.1016/0004-3702(82)90040-6

N. Roy and A. Mccallum, Toward optimal active learning through sampling estimation of error reduction, Proc. 18th International Conf. on Machine Learning, pp.441-448, 2001.

N. Sauer, On the density of families of sets, Journal of Combinatorial Theory, Series A, vol.13, issue.1, pp.145-147, 1972.
DOI : 10.1016/0097-3165(72)90019-2

G. Schohn and D. Cohn, Less is more: Active learning with support vector machines, Proceedings of the Seventeenth International Conference on Machine Learning, pp.285-286, 2000.

H. S. Seung, M. Opper, and H. Sompolinsky, Query by committee, Proceedings of the fifth annual workshop on Computational learning theory , COLT '92, pp.287-294, 1992.
DOI : 10.1145/130385.130417

M. Sugiyama and N. Rubens, A batch ensemble approach to active learning with model selection, Neural Networks, vol.21, issue.9, pp.1278-1286, 2008.
DOI : 10.1016/j.neunet.2008.06.004

V. Vapnik, The Nature of Statistical Learning Theory, 1995.

M. Vidyasagar, A Theory of Learning and Generalization, 1997.