J. Thomas, C. Rose, and F. Charpillet, A Multi-HMM Approach to ECG Segmentation, 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06), pp.609-616, 2006.
DOI : 10.1109/ICTAI.2006.17

URL : https://hal.archives-ouvertes.fr/inria-00095437

A. Dib, C. Rose, and F. Charpillet, Bayesian 3D Human Motion Capture Using Factored Particle Filtering, 2010 22nd IEEE International Conference on Tools with Artificial Intelligence, 2010.
DOI : 10.1109/ICTAI.2010.131

URL : https://hal.archives-ouvertes.fr/inria-00546925

C. Rose, J. Saboune, and F. Charpillet, Reducing particle filtering complexity for 3d motion capture using dynamic bayesian networks, AAAI'08 : Proceedings of the 23rd national conference on Artificial intelligence, pp.1396-1401, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00332714

C. Rose, C. Smaili, and F. Charpillet, A dynamic Bayesian network for handling uncertainty in a decision support system adapted to the monitoring of patients treated by hemodialysis, 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05), 2005.
DOI : 10.1109/ICTAI.2005.7

URL : https://hal.archives-ouvertes.fr/inria-00000477

C. Rose and F. Charpillet, Apprentissage d'une discrétisation pour construire une politique à partir d'exemples, Acte des Journées Francophones Planification Décision Apprentissage, 2009.

C. Smaili and F. Charpillet, Maan El Badaoui El Najjar, and Cédric Rose Multisensor fusion for mono and multi-vehicle localization using Bayesian network, Tools in Artificial Intelligence. IN-TEH, 2008.

J. Saboune, C. Rose, and F. Charpillet, Factored Interval Particle Filtering for Gait Analysis, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007.
DOI : 10.1109/IEMBS.2007.4353018

URL : https://hal.archives-ouvertes.fr/inria-00170996

J. Thomas, C. Rose, and F. Charpillet, A Support System for ECG Segmentation Based on Hidden Markov Models, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007.
DOI : 10.1109/IEMBS.2007.4353017

URL : https://hal.archives-ouvertes.fr/inria-00170988

C. Smaili, C. Rose, and F. Charpillet, A decision support system for the monitoring of patients treated by hemodialysis based on a bayesian network
URL : https://hal.archives-ouvertes.fr/inria-00000478

. Auvinet, Gait Abnormalities in Elderly Fallers, Journal of Aging and Physical Activity, vol.11, issue.1, 2003.
DOI : 10.1123/japa.11.1.40

]. Baird, Residual Algorithms: Reinforcement Learning with Function Approximation, Proceedings of the Twelfth International Conference on Machine Learning, pp.30-37, 1995.
DOI : 10.1016/B978-1-55860-377-6.50013-X

. Barrow, Parametric correspondence and chamfer matching : two new techniques for image matching, IJCAI'77 : Proceedings of the 5th international joint conference on Artificial intelligence, pp.659-663, 1977.

. Baum, A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains, The Annals of Mathematical Statistics, vol.41, issue.1, pp.164-171, 1970.
DOI : 10.1214/aoms/1177697196

. Bradtke, Linear leastsquares algorithms for temporal difference learning, Machine Learning, pp.22-33, 1996.
DOI : 10.1007/978-0-585-33656-5_4

URL : http://www-anw.cs.umass.edu/pubs/1995_96/bradtke_b_ML96.pdf

]. and R. Cassandra, Exact and approximate algorithms for partially observable markov decision processes, 1998.

]. Chrisman, Reinforcement learning with perceptual aliasing : The perceptual distinctions approach, Proceedings of the Tenth National Conference on Artificial Intelligence, pp.183-188, 1992.

. Deutscher, Articulated body motion capture by annealed particle filtering, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662), pp.126-133, 2000.
DOI : 10.1109/CVPR.2000.854758

URL : http://cs.gmu.edu/~zduric/it835/Papers/cvpr2000-deutscher.pdf

. Engel, Reinforcement learning with Gaussian processes, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.201-208, 2005.
DOI : 10.1145/1102351.1102377

URL : http://www-ee.technion.ac.il/~rmeir/Publications/EngelMannorMeirICML05.pdf

. Ernst, Tree-based batch mode reinforcement learning, Journal of Machine Learning Research, vol.6, pp.503-556, 2005.

. Falkhausen, Calculation of distance measures between hidden markov models, Proceedings of Eurospeech, pp.1487-1490, 1995.

. Fine and . Singer, Shai Fine and Yoram Singer. The hierarchical hidden markov model : Analysis and applications, MACHINE LEARNING, pp.41-62, 1998.

C. Fung, M. Robert, K. Fung, and . Chang, Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks, UAI '89 : Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence, pp.209-220, 1990.
DOI : 10.1016/B978-0-444-88738-2.50023-3

P. Geist, Matthieu Geist and Olivier Pietquin. A brief survey of parametric value function approximation, 2010.

. Geist, Kalman Temporal Differences: The deterministic case, 2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, pp.185-192, 2009.
DOI : 10.1109/ADPRL.2009.4927543

URL : https://hal.archives-ouvertes.fr/hal-00380870

. Graja, ]. S. Boucher, J. Graja, and . Boucher, Markov models for automated ECG interval analysis, WISP 2003 : IEEE International Symposium on Inteligent Signal Processing, pp.105-109, 2003.

S. Heckerman, E. David, E. H. Heckerman, and . Shortliffe, From certainty factors to belief networks, Artificial Intelligence in Medicine, vol.4, issue.1, pp.35-52, 1992.
DOI : 10.1016/0933-3657(92)90036-O

D. Huang, A. Huang, and . Darwiche, Inference in belief networks: A procedural guide, International Journal of Approximate Reasoning, vol.15, issue.3, pp.225-263, 1996.
DOI : 10.1016/S0888-613X(96)00069-2

. Hughes, Markov models for automated ECG interval analysis, Advances in Neural Information Processing Systems 16, 2004.

B. Isard, Condensation ? conditional density propagation for visual tracking, International Journal of Computer Vision, vol.29, issue.1, pp.5-28, 1998.
DOI : 10.1023/A:1008078328650

. Jain, Data clustering: a review, ACM Computing Surveys, vol.31, issue.3, pp.264-323, 1999.
DOI : 10.1145/331499.331504

J. Laurent, Apprentissage et adaptation pour la modélisation stochastique de systèmes dynamiques réels, 2002.

. Jensen, Bayesian updating in causal probabilistic networks by local computations, Computational Statistics Quaterly, vol.4, pp.269-282, 1990.

. Khawaja, A wavelet-based multi-channel ECG delineator, Proceedings of The 3rd European Medical and Biological Engineering Conference, 2005.

. Ki?merová, Classification of ECG signals using wavelet transform and hidden Markov models, Proceedings of The 3 rd European Medical and Biological Engineering Conference, 2005.

. Köhler, The principles of software QRS detection, IEEE Engineering in Medicine and Biology Magazine, vol.21, issue.1, 2002.
DOI : 10.1109/51.993193

. Krogh, Hidden Markov Models in Computational Biology, Journal of Molecular Biology, vol.235, issue.5, pp.1501-1531, 1994.
DOI : 10.1006/jmbi.1994.1104

S. L. Lauritzen, D. J. Lauritzen, and . Spiegelhalter, Local computations with probabilities on graphical structures and their application to expert systems, Journal of the Royal Statistical Society. Series B (Methodological), vol.50, issue.2, pp.157-224, 1988.

W. L. Lauritzen, N. Lauritzen, and . Wermuth, Graphical Models for Associations between Variables, some of which are Qualitative and some Quantitative, The Annals of Statistics, vol.17, issue.1, pp.31-57, 1989.
DOI : 10.1214/aos/1176347003

L. Steffen and . Lauritzen, Propagation of probabilities, means and variances in mixed graphical association models, Journal of the American Statistical Association, vol.87, pp.1098-1108, 1992.

. Li, . C. Biswas, G. Li, and . Biswas, A bayesian approach to temporal data clustering using hidden markov models, Proceedings of the Seventeenth International Conference on Machine Learning, pp.543-550, 2000.

. Littman, Learning policies for partially observable environments: Scaling up, Proceedings of the Twelfth International Conference on Machine Learning, pp.362-370, 1995.
DOI : 10.1016/B978-1-55860-377-6.50052-9

. Mannor, Bias and variance in value function estimation, Twenty-first international conference on Machine learning , ICML '04, pp.308-322, 2004.
DOI : 10.1145/1015330.1015402

. Martinez, A Wavelet-Based ECG Delineator: Evaluation on Standard Databases, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING BME, 2004.
DOI : 10.1109/TBME.2003.821031

]. R. Mccallum, First results with utile distinction memory for reinforcement learning, 1992.

I. Mccormick, M. Mccormick, and . Isard, Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking, ECCV '00 : Proceedings of the 6th European Conference on Computer Vision-Part II, pp.3-19, 2000.
DOI : 10.1007/3-540-45053-X_1

]. and P. Murphy, Dynamic Bayesian Networks : Representation, Inference and Learning, 2002.

B. Nedi?, D. P. Nedi?, and . Bertsekas, Least squares policy evaluation algorithms with linear function approximation, Theory and Applications, vol.13, pp.79-110, 2002.

]. Paysant, Processus d'adaptation de la marche : l'apprentissage écocontraint , influences des conditions de terrain sur la marche de l'amputé tibial, 2006.

]. L. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE, pp.257-286, 1989.

C. Saboune, F. Saboune, and . Charpillet, Using interval particle filtering for markerless 3d human motion capture, Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence, pp.621-627, 2005.

]. Saboune, Développement d'un système passif de suivi 3D du mouvement humain par filtrage particulaire Estimating the dimension of a model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.

. Shachter, D. Andersen-]-ross, S. K. Shachter, and . Andersen, Global Conditioning for Probabilistic Inference in Belief Networks, Proceedings of the Tenth Conference on Uncertainty in AI, pp.514-522, 1994.
DOI : 10.1016/B978-1-55860-332-5.50070-5

. Shachter, D. Peot-]-ross, M. A. Shachter, and . Peot, Simulation Approaches to General Probabilistic Inference on Belief Networks, UAI '89 : Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence, pp.221-234, 1990.
DOI : 10.1016/B978-0-444-88738-2.50024-5

D. Ross and . Shachter, Bayes-ball : The rational pastime (for determining irrelevance and requisite information in belief networks and influence diagrams), Proceedings of the Fourteenth Conference in Uncertainty in Artificial Intelligence, pp.480-487, 1998.

. Shortliffe, . Buchanan, H. Edward, B. G. Shortliffe, and . Buchanan, A model of inexact reasoning in medicine, Mathematical Biosciences, vol.23, pp.3-4351, 1975.

A. Simon, D. Simon, and . Acker, La place de la télémédecine dans l'organisation des soins Ministère de la Santé et des Sports, Direction de l'Hospitalisation et de l'Organisation des Soins, nov, 2008.

D. William and . Smart, Making reinforcement learning work on real robots, 2002.

F. Sobel, G. Sobel, and . Feldman, A 3x3 isotropic gradient operator for image processing, 1968.

. Sutton, Fast gradient-descent methods for temporal-difference learning with linear function approximation, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, 2009.
DOI : 10.1145/1553374.1553501

D. Watkins, Chris Watkins and Peter Dayan. Q-learning, Machine Learning, pp.279-292, 1992.

Y. , B. Yu, and D. P. Bertsekas, Q-learning algorithms for optimal stopping based on least squares, Proceedings of European Control Conference, 2007.