E. L. Lehmann, Nonparametrics: Statistical Methods Based on Ranks, 2006.

I. Nouretdinov, S. G. Costafreda, A. Gammerman, A. Chervonenkis, V. Vovk et al., Machine learning classification with confidence: Application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression, NeuroImage, vol.56, issue.2, pp.809-813, 2011.
DOI : 10.1016/j.neuroimage.2010.05.023

H. Papadopoulos, V. Vovk, and A. Gammerman, Qualified predictions for large data sets in the case of pattern recognition, Proceedings of the First International Conference on Machine Learning and Applications, pp.159-163, 2002.

P. Simard, Y. Lecun, and J. Denker, Efficient pattern recognition using a new transformation distance, Advances in Neural Information Processing Systems, pp.50-58, 1993.

V. N. Vapnik, Statistical Learning Theory, 1998.

V. N. Vapnik and A. Y. Chervonenkis, Theory of Pattern Recognition (in Russian ), German translation: Theorie der Zeichenerkennung, 1974.

V. Vovk, On-line confidence machines are well-calibrated, The 43rd Annual IEEE Symposium on Foundations of Computer Science, 2002. Proceedings., pp.187-196, 2002.
DOI : 10.1109/SFCS.2002.1181895
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.100.1065

V. Vovk, A. Gammerman, and G. Shafer, Algorithmic Learning in a Random World, 2005.

V. Vovk, Transductive conformal predictors, On-line Compression Modelling project (New Series), 2013.
DOI : 10.1007/978-3-642-41142-7_36

F. Wilcoxon, Individual Comparisons by Ranking Methods, Biometrics Bulletin, vol.1, issue.6, pp.80-83, 1945.
DOI : 10.2307/3001968