, References

N. Betzler and B. Dorn, Towards a dichotomy for the Possible Winner problem in elections based on scoring rules, Journal of Computer and System Sciences, vol.76, issue.8, pp.812-836, 2010.
DOI : 10.1016/j.jcss.2010.04.002

T. Cour, B. Sapp, C. Jordan, and B. Taskar, Learning from ambiguously labeled images, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.919-926, 2009.
DOI : 10.1109/CVPR.2009.5206667

T. Cour, B. Sapp, and B. Taskar, Learning from partial labels, The Journal of Machine Learning Research, vol.12, pp.1501-1536, 2011.

E. Hüllermeier and J. Beringer, Learning from ambiguously labeled examples, Intelligent Data Analysis, vol.10, issue.5, pp.419-439, 2006.

A. J. Joshi, F. Porikli, and N. Papanikolopoulos, Coverage optimized active learning for k - NN classifiers, 2012 IEEE International Conference on Robotics and Automation, pp.5353-5358, 2012.
DOI : 10.1109/ICRA.2012.6225054

K. Konczak, L. , and J. , Voting procedures with incomplete preferences, Proc. IJCAI-05 Multidisciplinary Workshop on Advances in Preference Handling, 2005.

D. D. Lewis and J. Catlett, Heterogeneous Uncertainty Sampling for Supervised Learning, Proceedings of the eleventh international conference on machine learning, pp.148-156, 1994.
DOI : 10.1016/B978-1-55860-335-6.50026-X

L. Liu and T. Dietterich, Learnability of the superset label learning problem, Proceedings of the 31st International Conference on Machine Learning (ICML-14), pp.1629-1637, 2014.

H. Moulin, F. Brandt, V. Conitzer, U. Endriss, J. Lang et al., Handbook of Computational Social Choice, 2016.

B. Settles and M. Craven, An analysis of active learning strategies for sequence labeling tasks, Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP '08, pp.1070-1079, 2008.
DOI : 10.3115/1613715.1613855

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

X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang et al., Top 10 algorithms in data mining, Knowledge and Information Systems, vol.9, issue.2, pp.1-37, 2008.
DOI : 10.1017/CBO9780511815478

L. Xia and V. Conitzer, Determining Possible and Necessary Winners Given Partial Orders, Journal of Artificial Intelligence Research, vol.41, pp.25-67, 2011.
DOI : 10.1613/jair.3186

F. Yang and P. Vozila, Semi-Supervised Chinese Word Segmentation Using Partial-Label Learning With Conditional Random Fields, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.90-98, 2014.
DOI : 10.3115/v1/D14-1010

M. Zhang, Y. , and F. , Solving the partial label learning problem: an instance-based approach, Proceedings of the 24th International Conference on Artificial Intelligence, pp.4048-4054, 2015.