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

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.454.4681

T. Hoßfeld and . Others, Internet Video Delivery in YouTube: From Traffic Measurements to Quality of Experience, 2013.
DOI : 10.1007/978-3-642-36784-7_11

. Georgios-exarchakos-menkovski, A. Vlado, and . Liotta, Adaptive testing for video quality assessment, Proceedings of Quality of Experience for Multimedia Content Sharing, 2011.

V. Menkovski and . Others, Tackling the Sheer Scale of Subjective QoE, International Conference on Mobile Multimedia Communications, 2011.
DOI : 10.1364/JOSAA.24.003418

K. P. Ricky, Mok and others. Measuring the quality of experience of HTTP video streaming, Integrated Network Management, 2011.

F. Pedregosa, Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

L. Rizzo, Dummynet, ACM SIGCOMM Computer Communication Review, vol.27, issue.1, pp.31-41, 1997.
DOI : 10.1145/251007.251012

B. Settles, Active Learning Literature Survey, Computer Sciences, 2010.

T. Spetebroot, S. Afra, N. Aguilera, D. Saucez, and C. Barakat, From networklevel measurements to expected quality of experience: The Skype use case, Measurements Networking (M & N), 2015 IEEE International Workshop on
URL : https://hal.archives-ouvertes.fr/hal-01071373

C. Byron, Wallace and others. Active Learning for Biomedical Citation Screening, Proceedings of the 16th ACM SIGKDD, 2010.

F. Ting, . Wu, and . Others, Probability Estimates for Multi-class Classiication by Pairwise Coupling, J. Mach. Learn. Res, p.31, 2004.

P. Ye and D. Doermann, Active Sampling for Subjective Image Quality Assessment, 2014 IEEE Conference on Computer Vision and Pattern Recognition
DOI : 10.1109/CVPR.2014.541