A. Berlinet and C. Thomas-agnan, Reproducing kernel Hilbert spaces in probability and statistics, 2004.
DOI : 10.1007/978-1-4419-9096-9

S. Canu, Y. Grandvalet, V. Guigue, and A. Rakotomamonjy, Svm and kernel methods matlab toolbox, Perception Systemes et Information, 2005.

P. Chan and M. Mahoney, Modeling Multiple Time Series for Anomaly Detection, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005.
DOI : 10.1109/ICDM.2005.101

V. Chandola, A. Banerjee, and V. Kumar, Anomaly detection, ACM Computing Surveys, vol.41, issue.3, pp.1-1558, 2009.
DOI : 10.1145/1541880.1541882

K. Das, J. Schneider, and D. B. Neill, Anomaly pattern detection in categorical datasets, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 08, pp.169-176, 2008.
DOI : 10.1145/1401890.1401915

L. Debnath and P. Mikusi´nskimikusi´nski, Hilbert Spaces with Applications, 2005.

C. A. Floudas and P. M. Pardalos, Encyclopedia of optimization, 2008.

K. Fukumizu, A. Gretton, X. Sun, and B. Schölkopf, Kernel measures of conditional dependence, Advances in Neural Information Processing Systems, pp.489-496, 2008.

M. G. Genton, Classes of kernels for machine learning: A statistics perspective, J. Mach. Learn. Res, vol.2, pp.299-312, 2002.

T. Graepel and R. Herbrich, Invariant pattern recognition by semidefinite programming machines, Advances in Neural Information Processing Systems, p.2004, 2003.

M. Grant and S. Boyd, CVX: Matlab software for disciplined convex programming, version 2.1, 2014.

A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola, A kernel two-sample test, The Journal of Machine Learning Research, vol.13, pp.723-773, 2012.

C. Guilbart, Produits scalaires sur l'espace des mesures, Annales de l'institut Henri Poincaré (B) Probabilités et Statistiques, pp.333-354, 1979.

E. Keogh, J. Lin, and A. Fu, HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005.
DOI : 10.1109/ICDM.2005.79

R. Kondor and T. Jebara, A kernel between sets of vectors, ICML, pp.361-368, 2003.

I. Laptev, On Space-Time Interest Points, International Journal of Computer Vision, vol.17, issue.8, pp.107-123, 2005.
DOI : 10.1007/s11263-005-1838-7

D. G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, vol.60, issue.2, pp.91-110, 2004.
DOI : 10.1023/B:VISI.0000029664.99615.94

K. Muandet, K. Fukumizu, F. Dinuzzo, and B. Sch?a?sch?-sch?a?-ulkopf, Learning from distributions via support measure machines, Advances in Neural Information Processing Systems 25, pp.10-18, 2012.

K. Muandet and B. Schoelkopf, One-class support measure machines for group anomaly detection, Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-13), pp.449-458, 2013.

B. Póczos, L. Xiong, and J. G. Schneider, Nonparametric divergence estimation with applications to machine learning on distributions. CoRR, abs/1202, 2012.

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

B. Scholkopf, The kernel trick for distances Advances in neural information processing systems, pp.301-307, 2001.

B. Schölkopf, J. C. Platt, J. Shawe-taylor, A. J. Smola, and R. C. Williamson, Estimating the Support of a High-Dimensional Distribution, Neural Computation, vol.6, issue.1, pp.1443-1471, 2001.
DOI : 10.1214/aos/1069362732

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

P. K. Shivaswamy, C. Bhattacharyya, and A. J. Smola, Second order cone programming approaches for handling missing and uncertain data, J. Mach. Learn. Res, vol.7, pp.1283-1314, 2006.

A. Smola, A. Gretton, L. Song, and B. Schölkopf, A Hilbert Space Embedding for Distributions, Algorithmic Learning Theory, pp.13-31, 2007.
DOI : 10.1073/pnas.0601231103

B. K. Sriperumbudur, A. Gretton, K. Fukumizu, G. Lanckriet, and B. , Sch?A?Sch? Sch?A? ulkopf. Injective hilbert space embeddings of probability measures, In In COLT, 2008.

B. K. Sriperumbudur, A. Gretton, K. Fukumizu, B. Schölkopf, and G. R. Lanckriet, Hilbert space embeddings and metrics on probability measures, The Journal of Machine Learning Research, vol.99, pp.1517-1561, 2010.

C. Suquet, Distances euclidiennes sur les mesures signees et applications a des theoremes de berry-esseen, Bulletin of the Belgian Mathematical Society Simon Stevin, vol.2, issue.2, pp.161-182, 1995.

D. M. Tax and R. P. Duin, Support Vector Data Description, Machine Learning, vol.54, issue.1, pp.45-66, 2004.
DOI : 10.1023/B:MACH.0000008084.60811.49

L. Wernisch, S. L. Kendall, S. Soneji, A. Wietzorrek, T. Parish et al., Analysis of whole-genome microarray replicates using mixed models, Bioinformatics, vol.19, issue.1, pp.53-61, 2003.
DOI : 10.1093/bioinformatics/19.1.53

L. Xiong, B. Póczos, and J. G. Schneider, Group anomaly detection using flexible genre models, NIPS, pp.1071-1079, 2011.

L. Xiong, B. Póczos, J. G. Schneider, A. J. Connolly, and J. Vanderplas, Hierarchical probabilistic models for group anomaly detection, AISTATS, pp.789-797, 2011.

J. Yang and S. Gunn, Exploiting Uncertain Data in Support Vector Classification, Knowledge-Based Intelligent Information and Engineering Systems, pp.148-155, 2007.
DOI : 10.1007/978-3-540-74829-8_19

J. B. Zhang, Support vector classification with input data uncertainty, Advances in Neural Information Processing Systems 17: Proceedings of the 2004 Conference, p.161, 2005.