V. Vovk, A. Gammerman, and G. Shafer, Algorithmic learning in a random world, 2006.

H. Papadopoulos, Inductive Conformal Prediction: Theory and Application to Neural Networks, Tools in Artificial Intelligence, vol.18, pp.315-330, 2008.
DOI : 10.5772/6078

A. Gammerman, V. Vovk, and V. Vapnik, Learning by transduction, Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, pp.148-155, 1998.

C. Saunders, A. Gammerman, and V. Vovk, Transduction with confidence and credibility, Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI'99, pp.722-726, 1999.

H. Papadopoulos, K. Proedrou, V. Vovk, and A. Gammerman, Inductive Confidence Machines for Regression, Machine Learning: ECML 2002, pp.345-356, 2002.
DOI : 10.1007/3-540-36755-1_29

H. Papadopoulos, Inductive Conformal Prediction: Theory and Application to Neural Networks, Tools in artificial intelligence, vol.18, issue.2, pp.315-330, 2008.
DOI : 10.5772/6078

H. Papadopoulos, V. Vovk, and A. Gammerman, Conformal Prediction with Neural Networks, 19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007), pp.388-395, 2007.
DOI : 10.1109/ICTAI.2007.47

V. N. Balasubramanian, S. S. Ho, and V. Vovk, Conformal prediction for reliable machine learning: theory, adaptations, and applications, 2013.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in python, The Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

U. Johansson, H. Boström, and T. Löfström, Conformal Prediction Using Decision Trees, 2013 IEEE 13th International Conference on Data Mining, 2013.
DOI : 10.1109/ICDM.2013.85

C. C. Chang and C. J. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3
DOI : 10.1145/1961189.1961199

K. Bache and M. Lichman, UCI machine learning repository, 2013.

L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen, Classification and regression trees, 1984.

L. Breiman, Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324

C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, vol.1, issue.3, pp.273-297, 1995.
DOI : 10.1007/BF00994018

I. Buciu, C. Kotropoulos, and I. Pitas, Demonstrating the stability of support vector machines for classification, Signal Processing, vol.86, issue.9, pp.2364-2380, 2006.
DOI : 10.1016/j.sigpro.2005.11.005

D. Devetyarov and I. Nouretdinov, Prediction with Confidence Based on a Random Forest Classifier, In: Artificial Intelligence Applications and Innovations, pp.37-44, 2010.
DOI : 10.1007/978-3-642-16239-8_8

URL : https://hal.archives-ouvertes.fr/hal-01060649

E. P. Costa, S. Verwer, and H. Blockeel, Estimating Prediction Certainty in Decision Trees, pp.138-149, 2013.
DOI : 10.1007/978-3-642-41398-8_13