I. H. Bae, M. G. Na, Y. J. Lee, and G. C. Park, Calculation of the power peaking factor in a nuclear reactor using support vector regression models, Annals of Nuclear Energy, vol.35, issue.12, pp.2200-2205, 2008.
DOI : 10.1016/j.anucene.2008.09.004

J. J. Cai, Applying support vector machine to predict the critical heat flux in concentric-tube open thermosiphon, Annals of Nuclear Energy, vol.43, 2012.
DOI : 10.1016/j.anucene.2011.12.029

W. Chu, S. Keerthi, and C. J. Ong, Bayesian Support Vector Regression Using a Unified Loss Function, IEEE Transactions on Neural Networks, vol.15, issue.1, pp.1-14, 2002.
DOI : 10.1109/TNN.2003.820830

O. Elnokity, I. I. Mahmoud, M. K. Refai, and H. M. Farahat, ANN based Sensor Faults Detection, Isolation, and Reading Estimates ??? SFDIRE: Applied in a nuclear process, Annals of Nuclear Energy, vol.49, pp.131-142, 2012.
DOI : 10.1016/j.anucene.2012.06.003

J. B. Gao, S. R. Gunn, C. J. Harris, and M. Brown, A Probabilistic Framework for SVM Regression and Error Bar Estimation, Mach. Learn, vol.46, pp.1-3, 2001.

D. S. Kim, J. H. Kim, M. Gyunna, and J. W. Kim, UNCERTAINTY ANALYSIS OF DATA-BASED MODELS FOR ESTIMATING COLLAPSE MOMENTS OF WALL-THINNED PIPE BENDS AND ELBOWS, Nuclear Engineering and Technology, vol.44, issue.3, pp.323-330, 2012.
DOI : 10.5516/NET.09.2011.032

J. Liu, R. Seraoui, V. Vitelli, and E. Zio, Nuclear power plant components condition monitoring by probabilistic support vector machine, Annals of Nuclear Energy, vol.56, 2013.
DOI : 10.1016/j.anucene.2013.01.005
URL : https://hal.archives-ouvertes.fr/hal-00790421

M. G. Na, J. W. Kim, and D. N. Moreton, Estimation of collapse moment for the wall-thinned pipe bends using fuzzy model identification, Nuclear Engineering and Design, vol.236, issue.13, pp.1335-1343, 2006.
DOI : 10.1016/j.nucengdes.2005.12.003

A. J. Smola and B. Schölkopf, A tutorial on support vector regression, Statistics and Computing, vol.14, issue.3, pp.199-222, 2004.
DOI : 10.1023/B:STCO.0000035301.49549.88

K. Trontl, T. Smuc, and D. Pevec, Support vector regression model for the estimation of ??-ray buildup factors for multi-layer shields, Annals of Nuclear Energy, vol.34, issue.12, pp.939-952, 2007.
DOI : 10.1016/j.anucene.2007.05.001

V. N. Vapnik, S. E. Golowich, and A. Smola, Support vector method for function approximation, regression estimation, and signal processing, Proceedings of the 10th Neural Information Processing Systems (NIPS) Conference, 1996.

V. Venkatasubramanian, Prognostic and diagnostic monitoring of complex systems for product lifecycle management: Challenges and opportunities, Computers & Chemical Engineering, vol.29, issue.6, pp.1253-1263, 2005.
DOI : 10.1016/j.compchemeng.2005.02.026

E. Zio, Diagnostics and prognostics of engineering systems: methods and techniques, Hershey: Engineering Science Reference, 2012.

E. Zio and F. Di-maio, A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system, Reliability Engineering & System Safety, vol.95, issue.1, pp.45-57, 2010.
DOI : 10.1016/j.ress.2009.08.001

E. Zio, D. Maio, F. Stasi, and M. , A data-driven approach for predicting failure scenarios in nuclear systems, Annals of Nuclear Energy, vol.37, issue.4, pp.482-491, 2010.
DOI : 10.1016/j.anucene.2010.01.017
URL : https://hal.archives-ouvertes.fr/hal-00606970