V. Chandola, A. Banerjee, and V. Kumar, Anomaly detection, ACM Computing Surveys, vol.41, issue.3, pp.1-58, 2009.
DOI : 10.1145/1541880.1541882

W. Polonik, Minimum volume sets and generalized quantile processes, Stochastic Processes and their Applications, pp.1-24, 1997.
DOI : 10.1016/S0304-4149(97)00028-8

URL : http://doi.org/10.1016/s0304-4149(97)00028-8

J. H. Einmahl and D. Mason, Generalized Quantile Processes, The Annals of Statistics, vol.20, issue.2, pp.1062-1078, 1992.
DOI : 10.1214/aos/1176348670

A. Baíllo, Total error in a plug-in estimator of level sets, Statistics & Probability Letters, vol.65, issue.4, pp.411-417, 2003.
DOI : 10.1016/j.spl.2003.08.007

B. Cadre, Kernel estimation of density level sets, Journal of Multivariate Analysis, vol.97, issue.4, pp.999-1023, 2006.
DOI : 10.1016/j.jmva.2005.05.004

URL : https://hal.archives-ouvertes.fr/hal-00003898

B. Cadre, B. Pelletier, and P. Pudlo, Estimation of density level sets with a given probability content, Journal of Nonparametric Statistics, vol.69, issue.1, pp.261-272, 2013.
DOI : 10.1080/10485252.2012.750319

URL : https://hal.archives-ouvertes.fr/hal-00796621

C. D. Scott and R. D. Nowak, Learning minimum volume sets, Journal of Machine Learning Research, vol.7, pp.665-704, 2006.

M. A. Davenport, R. Baraniuk, and C. Scott, Learning Minimum Volume Sets with Support Vector Machines, 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, pp.301-306, 2006.
DOI : 10.1109/MLSP.2006.275565

B. Schölkopf, J. Platt, A. J. Shawe-taylor, J. Smola, and R. C. Williamson, Estimating the Support of a High-Dimensional Distribution, Neural Computation, vol.6, issue.1, 2001.
DOI : 10.1214/aos/1069362732

D. M. Tax and R. P. Duin, Support Vector Data Description, Machine Learning, pp.45-66, 2004.
DOI : 10.1023/B:MACH.0000008084.60811.49

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

D. M. Tax, One-class classification, Ph.D Dissertation, 2001.

T. Hastie, R. Tibshirani, and F. J. , The Elements Of Statistical Learning: Data Mining, Inference and Prediction, 2009.

I. Steinwart, D. Hush, and C. Scovel, A classification framework for anomaly detection, Journal of Machine Learning Research, vol.6, pp.211-232, 2005.

R. Vert and J. Vert, Consistency and convergence rates of one-class SVMs and related algorithms, Journal of Machine Learning Research, vol.7, pp.1469-1480, 2006.

M. P. Martínez, E. Vazquez, E. Walter, and G. Fleury, RKHS classification for multivariate extreme-value analysis, 2008.

F. Filippone, M. Masulli, and S. Rovetta, Applying the Possibilistic c-Means Algorithm in Kernel-Induced Spaces, IEEE Transactions on Fuzzy Systems, vol.18, issue.3, pp.572-584, 2010.
DOI : 10.1109/TFUZZ.2010.2043440

A. Glazer, M. Lindenbaum, and M. S. , Learning high-density regions for a generalized kolmogorov-smirnov test in high-dimensional data, Proceedings of The 26th Conference on Neural Information Processing Systems, 2012.

A. Glazer, M. Lindenbaum, and S. Markovitch, q-OCSVM: A qquantile estimator for high-dimensional distributions, Advances in Neural Information Processing Systems 26, pp.503-511, 2013.

L. Devroye, L. Györfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition, 1996.
DOI : 10.1007/978-1-4612-0711-5

G. Lee and C. Scott, The One Class Support Vector Machine Solution Path, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, pp.521-524, 2007.
DOI : 10.1109/ICASSP.2007.366287

P. Rigollet and R. Vert, Optimal rates for plug-in estimators of density level sets, Bernoulli, vol.15, issue.4, pp.1154-1178, 2009.
DOI : 10.3150/09-BEJ184

D. M. Tax and R. P. Duin, Uniform object generation for optimizing one-class classifiers, Journal for Machine Learning Research, pp.155-173, 2001.

S. Clémençon and J. Jakubowicz, Scoring anomalies: a m-estimation formulation, AISTATS '13: Sixteenth international conference on Artificial Intelligence and Statistics, pp.659-667, 2013.

S. Clémençon and S. Robbiano, Anomaly ranking as supervised bipartite ranking, Proceedings of the 31th International Conference on Machine Learning, ICML 2014, pp.21-26, 2014.

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

C. Chang and C. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, pp.1-2727, 2011.
DOI : 10.1145/1961189.1961199

D. Harrison and D. L. Rubinfeld, Hedonic housing prices and the demand for clean air, Journal of Environmental Economics and Management, vol.5, issue.1, pp.81-102, 1978.
DOI : 10.1016/0095-0696(78)90006-2