A. Gammerman and A. R. Thatcher, Bayesian Diagnostic Probabilities without Assuming Independence of Symptoms, Methods Inf Med, vol.30, issue.1, pp.15-22, 1991.

A. Gammerman and V. Vovk, Hedging Predictions in Machine Learning: The Second Computer Journal Lecture, The Computer Journal, vol.50, issue.2, pp.151-163, 2007.
DOI : 10.1093/comjnl/bxl065

H. Papadopoulos, Inductive Conformal Prediction: Theory and Application to Neural Networks, Tools in Artificial, vol.Intelligence, pp.315-329, 2008.
DOI : 10.5772/6078

H. Papadopoulos, A. Gammerman, and V. Vovk, Reliable Diagnosis of Acute Abdominal Pain with Conformal Prediction, Engineering Intelligent Systems, vol.17, issue.2-3, pp.127-137, 2009.

H. Papadopoulos, A. Gammerman, and V. Vovk, Confidence Predictions for the Diagnosis of Acute Abdominal Pain, Artificial Intelligence Applications and Innovations III, IFIP International Federation for Information Processing, pp.175-184
DOI : 10.1007/978-1-4419-0221-4_22

F. Yang, H. Wang, H. Mi, C. Lin, and W. Cai, Using random forest for reliable classification and cost-sensitive learning for medical diagnosis, BMC Bioinformatics, vol.10, issue.Suppl 1, p.22, 2009.
DOI : 10.1186/1471-2105-10-S1-S22

M. Yang, I. Nouretdinov, Z. Luo, and A. Gammerman, Feature Selection by Conformal Predictor, Artificial Intelligence Applications and Innovations, IFIP Advances in Information and Communication Technology, pp.439-448, 2011.
DOI : 10.1007/978-3-642-23960-1_51

L. Yu and H. Liu, Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution, Proceedings of the Twentieth International Conference on Machine Learning, 2003.

V. Vapnik and A. Vashist, A new learning paradigm: Learning using privileged information, Neural Networks, vol.22, issue.5-6, pp.544-557, 2009.
DOI : 10.1016/j.neunet.2009.06.042

V. Vapnik, A. Vashist, and N. Pavlovitch, Learning using hidden information: Master class learning. Proceedings of NATO workshop on mining massive data sets of security, pp.3-14, 2008.

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