J. R. Gage, M. H. Schwartz, S. E. Koop, and T. F. Novacheck, The Identification and Treatment of Gait Problems in Cerebral Palsy, 2009.

J. A. Reinbolt, M. D. Fox, M. H. Schwartz, and S. L. Delp, Predicting outcomes of rectus femoris transfer surgery, Gait & Posture, vol.30, issue.1, pp.100-105, 2009.
DOI : 10.1016/j.gaitpost.2009.03.008

A. S. Arnold, M. Q. Liu, M. H. Schwartz, S. Õunpuu, and S. L. Delp, The role of estimating muscle-tendon lengths and velocities of the hamstrings in the evaluation and treatment of crouch gait, Gait & Posture, vol.23, issue.3, pp.273-281, 2006.
DOI : 10.1016/j.gaitpost.2005.03.003

A. Sebsadji, N. Khouri, K. Djemal, D. Yepremian, F. Hareb et al., Description and classification of the effect of hamstrings lengthening in cerebral palsy children multi-site surgery, Computer Methods in Biomechanics and Biomedical Engineering, vol.28, issue.3, pp.177-179, 2012.
DOI : 10.1016/j.gaitpost.2008.05.001

M. H. Schwartz, A. Rozumalski, W. Truong, and T. F. Novacheck, Predicting the outcome of intramuscular psoas lengthening in children with cerebral palsy using preoperative gait data and the random forest algorithm, Gait & Posture, vol.37, issue.4, pp.473-479, 2013.
DOI : 10.1016/j.gaitpost.2012.08.016

J. L. Hicks, S. L. Delp, and M. H. Schwartz, Can biomechanical variables predict improvement in crouch gait?, Gait & Posture, vol.34, issue.2, pp.197-201, 2011.
DOI : 10.1016/j.gaitpost.2011.04.009

K. Sullivan, J. Richards, F. Miller, P. Castagno, and N. Lennon, Predicting the outcome of surgery for children with cerebral palsy using pre-operative gait analysis, Gait & Posture, vol.3, issue.2, p.92, 1995.
DOI : 10.1016/0966-6362(95)93476-S

L. A. Hersh, J. Q. Sun, J. G. Richards, and F. Miller, The prediction of post-operative gait patterns using neural networks, Gait & Posture, vol.5, issue.2, 1997.
DOI : 10.1016/S0966-6362(97)83371-1

O. A. Galarraga, C. V. Vigneron, B. Dorizzi, N. Khouri, and E. Desailly, Predicting postoperative knee flexion and pelvic tilt at initial contact of cerebral palsy children, Movement & Sport Sciences - Science & Motricit??, issue.93, pp.93-87, 2016.
DOI : 10.1097/01241398-200105000-00023
URL : https://hal.archives-ouvertes.fr/hal-01706383

T. A. Niiler, J. G. Richards, and F. Miller, Concurrent surgeries are a factor in predicting success of rectus transfer outcomes, Gait & Posture, vol.26, issue.1, pp.76-81, 2007.
DOI : 10.1016/j.gaitpost.2006.07.014

R. B. Davis, I. , S. Õunpuu, D. Tyburski, and J. R. Gage, A gait analysis data collection and reduction technique, Human Movement Science, vol.10, issue.5, pp.575-587, 1991.
DOI : 10.1016/0167-9457(91)90046-Z

M. P. Kadaba, H. K. Ramakrishnan, and M. E. Wootten, Measurement of lower extremity kinematics during level walking, Journal of Orthopaedic Research, vol.15, issue.3, pp.383-392, 1990.
DOI : 10.1080/00222895.1983.10735302

M. Templ, A. Kowarik, and P. Filzmoser, Iterative stepwise regression imputation using standard and robust methods, Computational Statistics & Data Analysis, vol.55, issue.10, pp.2793-2806, 2011.
DOI : 10.1016/j.csda.2011.04.012

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2000.

E. Desailly, Y. Daniel, P. Sardain, and P. Lacouture, Foot contact event detection using kinematic data in cerebral palsy children and normal adults gait, Gait & Posture, vol.29, issue.1, pp.76-80, 2009.
DOI : 10.1016/j.gaitpost.2008.06.009

M. H. Schwartz and A. Rozumalski, The gait deviation index: A new comprehensive index of gait pathology, Gait & Posture, vol.28, issue.3, pp.351-357, 2008.
DOI : 10.1016/j.gaitpost.2008.05.001

I. T. Jolliffe, Principal Component Analysis, 2002.

R. G. Miller, The jackknife-a review, Biometrika, vol.61, pp.1-15, 1974.

. Stuart, Kendall's Advanced Theory of Statistics: Distribution Theory, 1994.

R. Baker, J. L. Mcginley, M. H. Schwartz, S. Beynon, A. Rozumalski et al., The Gait Profile Score and Movement Analysis Profile, Gait & Posture, vol.30, issue.3, pp.265-269, 2009.
DOI : 10.1016/j.gaitpost.2009.05.020

S. Klejman, J. Andrysek, A. Dupuis, and V. Wright, Test-Retest Reliability of Discrete Gait Parameters in Children With Cerebral Palsy, Archives of Physical Medicine and Rehabilitation, vol.91, issue.5, pp.781-787, 2010.
DOI : 10.1016/j.apmr.2010.01.016

K. Desloovere, G. Molenaers, H. Feys, C. Huenaerts, B. Callewaert et al., Do dynamic and static clinical measurements correlate with gait analysis parameters in children with cerebral palsy?, Gait & Posture, vol.24, issue.3, pp.302-313, 2006.
DOI : 10.1016/j.gaitpost.2005.10.008

A. Bonnefoy-mazure, Y. Sagawa-jr, V. Pomero, P. Lascombes, G. De-coulon et al., Are clinical parameters sufficient to model gait patterns in patients with cerebral palsy using a multilinear approach?, Computer Methods in Biomechanics and Biomedical Engineering, vol.20, issue.7, pp.800-806, 2016.
DOI : 10.1016/S0966-6362(02)00069-3
URL : https://hal.archives-ouvertes.fr/hal-01454811

W. Richard and . Baker, Measuring Walking: A Handbook of Clinical Gait Analysis, 2013.

A. Banerjee, C. Krumpelman, J. Ghosh, S. Basu, and R. J. Mooney, Model-based overlapping clustering, Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining , KDD '05, pp.532-537, 2005.
DOI : 10.1145/1081870.1081932

C. M. Bishop, Model-based machine learning, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.28, issue.25, p.371, 1984.
DOI : 10.1002/sim.3680

C. M. Bishop, Pattern Recognition And Machine Learning, 2006.

L. Breiman, Bagging predictors, Machine Learning, vol.10, issue.2, pp.123-140, 1996.
DOI : 10.2307/1403680

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2000.

J. R. Gage, M. H. Schwartz, S. E. Koop, and T. F. Novacheck, The Identification and Treatment of Gait Problems in Cerebral Palsy, 2009.

O. A. Galarraga, C. , V. Vigneron, B. Dorizzi, N. Khouri et al., Predicting postoperative knee flexion during gait of cerebral palsy children, Computer Methods in Biomechanics and Biomedical Engineering, vol.18, issue.sup1, pp.1-2, 2015.
DOI : 10.1016/j.csda.2011.04.012
URL : https://hal.archives-ouvertes.fr/hal-01187018

A. Omar, C. Galarraga, B. Vincent-vigneron, N. Dorizzi, E. Khouri et al., Estimation of Postoperative Knee Flexion at Initial Contact of Cerebral Palsy Children using Neural Networks, Proceedings of the International Conference on Pattern Recognition Applications and Methods, pp.338-342, 2015.

R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton, Adaptive Mixtures of Local Experts, Neural Computation, vol.4, issue.1, pp.79-87, 1991.
DOI : 10.1162/neco.1989.1.2.281

I. T. Jolliffe, Principal Component Analysis, 2002.

J. C. David and . Mackay, Bayesian Interpolation, Neural Computation, vol.4, issue.16, pp.415-447, 1991.

J. David and . Mackay, The evidence for neural networks, Maximum Entropy and Bayesian Methods, pp.165-183, 1992.

R. G. Miller, The jackknife-a review, Biometrika, vol.61, issue.1, pp.1-15, 1974.

T. A. Niiler, J. G. Richards, F. Miller, J. Q. Sun, and P. Castagno, Reliability of predictions of post-operative gait in rectus transfer patients using FFT neural networks, Gait & Posture, vol.9, p.107, 1999.

R. Polikar, Ensemble based systems in decision making. Circuits and Systems Magazine, IEEE, vol.6, issue.3, pp.21-45, 2006.

R. E. Schapire, The strength of weak learnability, Machine learning, vol.5, issue.2, pp.197-227, 1990.

H. Michael, A. Schwartz, and . Rozumalski, The gait deviation index: A new comprehensive index of gait pathology, Gait & Posture, vol.28, issue.3, pp.351-357, 2008.

H. Michael, A. Schwartz, W. Rozumalski, T. F. Truong, and . Novacheck, Predicting the outcome of intramuscular psoas lengthening in children with cerebral palsy using preoperative gait data and the random forest algorithm, Gait & Posture, vol.37, issue.4, pp.473-479, 2013.

. Stuart, Kendall's Advanced Theory of Statistics: Distribution Theory, 1994.

M. Templ, A. Kowarik, and P. Filzmoser, EM-based stepwise regression imputation using standard and robust methods, 2010.

H. David and . Wolpert, Stacked Generalization, Neural Networks, vol.5, pp.241-259, 1992.

M. Dubois, Quenet & Dreyfus, 2007) or medical imaging (i.e. magnetic resonance or tomography (Kodewitz et al., 2013)), among others. Thereby, hopefully these methods will be improved to crucially help society with this sensible medical application, such as electrocardiography (Sameni, 2008.

Y. Abdel-aziz and H. Karara, Direct Linear Transformation from Comparator Coordinates into Object Space Coordinates in Close-Range Photogrammetry, Proceedings of the Symposium on Close-Range Photogrammetry, pp.1-18, 1971.
DOI : 10.14358/PERS.81.2.103

P. Accardo, Capute & Accardo's Neurodevelopmental Disabilities in Infancy and Childhood: Neurodevelopmental Diagnosis and Treatment, 2007.

H. Akaike, Information Theory and an Extension of the Maximum Likelihood Principle, Selected Papers of Hirotugu Akaike, pp.199-213, 1998.

L. Allano, B. Dorizzi, and S. Garcia-salicetti, A new protocol for multi-biometric systems' evaluation maintaining the dependencies between biometric scores, Pattern Recognition, vol.45, issue.1, pp.119-127, 2012.
DOI : 10.1016/j.patcog.2011.07.001
URL : https://hal.archives-ouvertes.fr/hal-00662394

P. D. Allison, Missing Data The SAGE Handbook of Quantitative Methods in Psychology, pp.72-89, 2009.

S. Armand, E. Watelain, M. Mercier, G. Lensel, and F. Lepoutre, Identification and classification of toe-walkers based on ankle kinematics, using a data-mining method, Gait & Posture, vol.23, issue.2, pp.240-248, 2006.
DOI : 10.1016/j.gaitpost.2005.02.007

C. G. Atkeson and S. Schaal, Memory-based neural networks for robot learning, Neurocomputing, vol.9, issue.3, pp.243-269, 1995.
DOI : 10.1016/0925-2312(95)00033-6

R. W. Baker, Measuring Walking: A Handbook of Clinical Gait Analysis, 2013.

C. M. Bishop, Pattern Recognition And Machine Learning, 2006.

C. M. Bishop, Model-based machine learning, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.28, issue.25, p.371, 1984.
DOI : 10.1002/sim.3680

A. Bonnefoy-mazure, Y. Sagawa, V. Pomero, P. Lascombes, G. De-coulon et al., Are clinical parameters sufficient to model gait patterns in patients with cerebral palsy using a multilinear approach?, Computer Methods in Biomechanics and Biomedical Engineering, vol.20, issue.7, pp.800-806, 2016.
DOI : 10.1016/S0966-6362(02)00069-3
URL : https://hal.archives-ouvertes.fr/hal-01454811

S. Boudaoud, Analyse de la variabilité de forme des signaux : Application aux signaux électrophysiologiques, 2006.

N. V. Boulgouris, D. Hatzinakos, and K. N. Plataniotis, Gait recognition: a challenging signal processing technology for biometric identification, IEEE Signal Processing Magazine, vol.22, issue.6, pp.22-78, 2005.
DOI : 10.1109/MSP.2005.1550191

L. Breiman, Bagging predictors, Machine Learning, vol.10, issue.2, pp.123-140, 1996.
DOI : 10.2307/1403680

L. Breiman, Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324

L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen, Classification and Regression Trees, 1984.

C. J. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, vol.2, issue.2, pp.121-167, 1998.
DOI : 10.1023/A:1009715923555

E. Cantoni and E. Ronchetti, Robust Inference for Generalized Linear Models, Journal of the American Statistical Association, vol.96, issue.455, pp.96-1022, 2001.
DOI : 10.1198/016214501753209004

A. Carriero, A. Zavatsky, J. Stebbins, T. Theologis, and S. J. Shefelbine, Determination of gait patterns in children with spastic diplegic cerebral palsy using principal components, Gait & Posture, vol.29, issue.1, pp.71-75, 2009.
DOI : 10.1016/j.gaitpost.2008.06.011

S. Chen, S. A. Billings, and W. Luo, Orthogonal least squares methods and their application to non-linear system identification, International Journal of Control, vol.10, issue.5, pp.1873-1896, 1989.
DOI : 10.2307/2284566

J. W. Chow and D. V. Knudson, Use of deterministic models in sports and exercise biomechanics research, Sports Biomechanics, vol.84, issue.3, pp.219-233, 2011.
DOI : 10.1053/S0003-9993(03)00280-6

J. Cleland, S. Koppenhaver, and F. H. Netter, Netter's orthopaedic clinical examination: an evidence-based approach, 2011.

I. Davis, R. B. Õunpuu, S. Tyburski, and D. Gage, A gait analysis data collection and reduction technique, Human Movement Science, vol.10, issue.5, pp.575-587, 1991.
DOI : 10.1016/0167-9457(91)90046-Z

D. Boor and C. , A Practical Guide to Splines, Applied Mathematical Sciences, vol.27, 2001.

P. A. Deluca, S. Ounpuu, R. B. Davis, and J. H. Walsh, Effect of Hamstring and Psoas Lengthening on Pelvic Tilt in Patients with Spastic Diplegic Cerebral Palsy, Journal of Pediatric Orthopaedics, vol.18, issue.6, pp.712-718, 1998.
DOI : 10.1097/01241398-199811000-00004

E. Desailly, Analyse biomécanique 3D de la marche de l'enfant déficient moteur : Modélisation segmentaire et modélisation musculo-squelettique, 2008.

E. Desailly, A. Sebsadji, D. Yepremian, K. Djemal, P. Hoppenot et al., Supervised classification of the effect of hamstrings lengthening in cerebral palsy children after single event multilevel surgery, Gait & Posture, vol.38, p.39, 2013.
DOI : 10.1016/j.gaitpost.2013.07.079

E. Desailly, A. Sebsadji, D. Yepremian, F. Hareb, and N. Khouri, Simulation of muscle retraction in cerebral palsy. Validation of a decision support system for surgical lengthening of contractured muscles, Computer Methods in Biomechanics and Biomedical Engineering, issue.S1, pp.15-263, 2012.

E. Desailly, D. Yepremian, N. Khouri, F. Hareb, L. Lejeune et al., Simulation of the muscles maximum extensibility by musculoskeletal modelling. Contribution to the differential diagnoses conducing to the decision of a surgical lengthening in cerebral palsy children, Gait & Posture, vol.30, p.136, 2009.
DOI : 10.1016/j.gaitpost.2009.08.203

A. R. Donders, G. J. Van-der-heijden, T. Stijnen, and K. G. Moons, Review: A gentle introduction to imputation of missing values, Journal of Clinical Epidemiology, vol.59, issue.10, pp.59-1087, 2006.
DOI : 10.1016/j.jclinepi.2006.01.014

B. Dorizzi, G. Pellieux, F. Jacquet, T. Czernichow, and A. Munoz, Variable selection using generalized RBF networks: Application to the forecast of the French T-bonds, CESA'96 IMACS Multiconference : computational engineering in systems applications, pp.122-127, 1996.

G. Dreyfus, J. Martinez, and M. Samuelides, Apprentissage statistique, 2008.

R. Dubois, P. Maison-blanche, B. Quenet, and G. Dreyfus, Automatic ECG wave extraction in long-term recordings using Gaussian mesa function models and nonlinear probability estimators, Computer Methods and Programs in Biomedicine, vol.88, issue.3, pp.217-233, 2007.
DOI : 10.1016/j.cmpb.2007.09.005

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2000.

M. A. El-yacoubi, A. Shaiek, and B. Dorizzi, HMM-based gait modeling and recognition under different walking scenarios, 2011 International Conference on Multimedia Computing and Systems, pp.1-5, 2011.
DOI : 10.1109/ICMCS.2011.5945573
URL : https://hal.archives-ouvertes.fr/hal-01302472

B. Elliott, Biomechanics and tennis, British Journal of Sports Medicine, vol.40, issue.5, pp.392-396, 2006.
DOI : 10.1136/bjsm.2005.023150
URL : http://europepmc.org/articles/pmc2577481?pdf=render

A. Farhangfar, L. Kurgan, and J. Dy, Impact of imputation of missing values on classification error for discrete data, Pattern Recognition, vol.41, issue.12, pp.41-3692, 2008.
DOI : 10.1016/j.patcog.2008.05.019

P. A. Federolf, K. A. Boyer, and T. P. Andriacchi, Application of principal component analysis in clinical gait research: Identification of systematic differences between healthy and medial knee-osteoarthritic gait, Journal of Biomechanics, vol.46, issue.13, pp.46-2173, 2013.
DOI : 10.1016/j.jbiomech.2013.06.032

M. Fernández-delgado, E. Cernadas, S. Barro, and D. Amorim, Do we need hundreds of classifiers to solve real world classification problems?, The Journal of Machine Learning Research, vol.15, issue.1, pp.3133-3181, 2014.

J. R. Gage, M. H. Schwartz, S. E. Koop, and T. F. Novacheck, The Identification and Treatment of Gait Problems in Cerebral Palsy, 2009.

J. R. Gage and J. L. Stout, Gait Analysis: Kinematics, Kinetics, Electromyography, Oxygen Consumption and Pedobarography The Identification and Treatment of Gait Problems in Cerebral Palsy, Clinics in Developmental Medicine, pp.180-181, 2009.

P. J. García-laencina, P. H. Abreu, M. H. Abreu, and N. Afonoso, Missing data imputation on the 5-year survival prediction of breast cancer patients with unknown discrete values, Computers in Biology and Medicine, vol.59, pp.125-133, 2015.
DOI : 10.1016/j.compbiomed.2015.02.006

S. R. Goldberg, S. Õunpuu, A. S. Arnold, J. R. Gage, and S. L. Delp, Kinematic and kinetic factors that correlate with improved knee flexion following treatment for stiff-knee gait, Journal of Biomechanics, vol.39, issue.4, pp.689-698, 2006.
DOI : 10.1016/j.jbiomech.2005.01.015

M. S. Grewal and A. P. Andrews, Kalman filtering theory and practice using MATLAB, 2001.

I. Guyon and A. Elisseeff, An introduction to variable and feature selection, The Journal of Machine Learning Research, vol.3, pp.1157-1182, 2003.

S. S. Haykin, Kalman filtering and neural networks, 2001.
DOI : 10.1002/0471221546

L. Hedström and E. B. Carlberg, Missing data in physiotherapists??? assessments of children with cerebral palsy, European Journal of Physiotherapy, vol.10, issue.59, pp.66-73, 2015.
DOI : 10.1111/j.1469-8749.2004.tb01021.x

Y. Hemo, M. D. Aiona, R. A. Pierce, R. Dorociak, and M. D. Sussman, Comparison of rectus femoris transposition with traditional transfer for treatment of stiff knee gait in patients with cerebral palsy, Journal of Children's Orthopaedics, vol.1, issue.1, pp.37-41, 2007.
DOI : 10.1007/s11832-006-0002-4

L. A. Hersh, J. Q. Sun, J. G. Richards, and F. Miller, The prediction of post-operative gait patterns using neural networks, Gait & Posture, vol.5, issue.2, p.151, 1997.
DOI : 10.1016/S0966-6362(97)83371-1

J. L. Hicks, S. L. Delp, and M. H. Schwartz, Can biomechanical variables predict improvement in crouch gait?, Gait & Posture, vol.34, issue.2, pp.197-201, 2011.
DOI : 10.1016/j.gaitpost.2011.04.009

N. Houmani, S. Garcia-salicetti, and B. Dorizzi, On measuring forgery quality in online signatures, Pattern Recognition, vol.45, issue.3, pp.1004-1018, 2012.
DOI : 10.1016/j.patcog.2011.08.019
URL : https://hal.archives-ouvertes.fr/hal-00663830

G. Huang, Q. Zhu, and C. Siew, Extreme learning machine: Theory and applications, Neurocomputing, vol.70, issue.1-3, pp.1-3, 2006.
DOI : 10.1016/j.neucom.2005.12.126

C. M. Hurvich and C. Tsai, Regression and time series model selection in small samples, Biometrika, vol.76, issue.2, pp.297-307, 1989.
DOI : 10.1093/biomet/76.2.297

R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton, Adaptive Mixtures of Local Experts, Neural Computation, vol.4, issue.1, pp.79-87, 1991.
DOI : 10.1162/neco.1989.1.2.281

A. A. Jamshidi, M. J. Kirby, and D. S. Broomhead, Geometric Manifold Learning, IEEE Signal Processing Magazine, vol.28, issue.2, pp.69-76, 2011.
DOI : 10.1109/MSP.2010.939550

S. Jennett, Churchill Livingstone's Dictionary of Sport and Exercise Science and Medicine, 2008.

J. M. Jerez, I. Molina, P. J. García-laencina, E. Alba, N. Ribelles et al., Missing data imputation using statistical and machine learning methods in a real breast cancer problem, Artificial Intelligence in Medicine, vol.50, issue.2, pp.105-115, 2010.
DOI : 10.1016/j.artmed.2010.05.002

X. Jiang, Linear Subspace Learning-Based Dimensionality Reduction, IEEE Signal Processing Magazine, vol.28, issue.2, pp.16-26, 2011.
DOI : 10.1109/MSP.2010.939041

I. Jolliffe, Principal Component Analysis. Springer Series in Statistics, 2002.

T. Kawata, Fourier Analysis in Probability Theory, Probability and Mathematical Statistics, 1972.

R. M. Kay, S. A. Rethlefsen, S. W. Dennis, and D. L. Skaggs, Prediction of postoperative gait velocity in cerebral palsy, Journal of Pediatric Orthopedics. Part B, vol.10, issue.4, pp.275-278, 2001.

N. Khouri and E. Desailly, Rectus femoris transfer in multilevel surgery: Technical details and gait outcome assessment in cerebral palsy patients, Orthopaedics & Traumatology: Surgery & Research, vol.99, issue.3, pp.333-340, 2013.
DOI : 10.1016/j.otsr.2012.10.017

A. Kodewitz, S. Lelandais, C. Montagne, and V. Vigneron, Alzheimer's disease early detection from sparse data using brain importance maps, ELCVIA Electronic Letters on Computer Vision and Image Analysis, vol.12, issue.1, 2013.
DOI : 10.5565/rev/elcvia.531
URL : https://hal.archives-ouvertes.fr/hal-01695694

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.9, issue.7553, pp.521-436, 2015.
DOI : 10.1007/s10994-013-5335-x

P. Legendre and L. F. Legendre, Numerical Ecology, 1998.
DOI : 10.1016/B978-0-12-409548-9.10595-0
URL : https://hal.archives-ouvertes.fr/hal-00530195

P. L. Leray, -. Gallinari, and P. , De l'utilisation d'OBD pour la s??lection de variables dans les perceptrons multicouches, Revue d'intelligence artificielle, vol.15, issue.3-4, pp.373-391, 2001.
DOI : 10.3166/ria.15.373-391

K. Levenberg, A method for the solution of certain non-linear problems in least squares, Quarterly of Applied Mathematics, vol.2, issue.2, pp.164-168, 1944.
DOI : 10.1090/qam/10666

F. Y. Ma, P. Selber, G. R. Nattrass, A. R. Harvey, R. Wolfe et al., Lengthening and transfer of hamstrings for a flexion deformity of the knee in children with bilateral cerebral palsy. Technique and preliminary results, Journal of Bone & Joint Surgery, vol.88, issue.2, pp.248-254, 2006.

D. J. Mackay, The Evidence for Neural Networks, Maximum Entropy and Bayesian Methods, pp.165-183, 1992.
DOI : 10.1007/978-94-017-2219-3_12

D. J. Mackay, Bayesian Interpolation, Neural Computation, vol.49, issue.3, pp.415-447, 1991.
DOI : 10.1093/comjnl/11.2.185

M. C. Marks and H. G. Chambers, Clinical utility of the Duncan-Ely test for rectus femoris dysfunction during the swing phase of gait, Developmental Medicine & Child Neurology, issue.11, pp.45-763, 2003.

R. A. Maronna, D. R. Martin, and V. J. Yohai, Robust Statistics, Theory and Methods, 2006.
DOI : 10.1002/0470010940

D. Marquardt, An Algorithm for Least-Squares Estimation of Nonlinear Parameters, Journal of the Society for Industrial and Applied Mathematics, vol.11, issue.2, pp.431-441, 1963.
DOI : 10.1137/0111030

W. S. Mcculloch and W. Pitts, A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, pp.115-133, 1943.

A. Menache, Understanding Motion Capture for Computer Animation and Video Games, 2000.

R. G. Miller, The jackknife-a review, Biometrika, vol.61, issue.1, pp.1-15, 1974.

K. Moran, C. Richter, and N. E. Connor, Letter to the editor regarding ???Application of principal component analysis in clinical gait research??? by Federolf and colleagues, Journal of Biomechanics, vol.47, issue.6, pp.47-1554, 2014.
DOI : 10.1016/j.jbiomech.2014.01.057

H. Motulsky and A. Christopoulos, Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting, 2004.

T. A. Niiler, Efficacy of predictions of post-operative gait in rectus transfer patients using neural networks, 2001.

T. A. Niiler, J. G. Richards, F. Miller, J. Q. Sun, and P. Castagno, Reliability of predictions of post-operative gait in rectus transfer patients using FFT neural networks, Proceedings of the Gait & Clinical Movement Analysis Society Conference, 1999.

T. F. Novacheck, Orthopaedic Treatment of Muscle Contractures The Identification and Treatment of Gait Problems in Cerebral Palsy, )., number 180-181 in Clinics in Developmental Medicine, pp.445-472, 2009.

M. C. Olsson, M. Krüger, L. Meyer, L. Ahnlund, L. Gransberg et al., Fibre type-specific increase in passive muscle tension in spinal cord-injured subjects with spasticity, The Journal of Physiology, vol.64, issue.1, pp.577-339, 2006.
DOI : 10.1016/S0006-3495(93)81482-6

M. Oskoui, F. Coutinho, J. Dykeman, N. Jetté, and T. Pringsheim, An update on the prevalence of cerebral palsy: a systematic review and meta-analysis, Developmental Medicine & Child Neurology, vol.31, issue.Suppl., pp.55-509, 2013.
DOI : 10.1093/ije/31.1.115

R. Palisano, P. Rosenbaum, S. Walter, D. Russell, E. Wood et al., Development and reliability of a system to classify gross motor function in children with cerebral palsy, Developmental Medicine & Child Neurology, vol.35, issue.4, pp.214-223, 1997.
DOI : 10.1001/jama.1952.02930180032007

R. J. Palisano, P. Rosenbaum, D. Bartlett, and M. H. Livingston, Content validity of the expanded and revised Gross Motor Function Classification System, Developmental Medicine & Child Neurology, vol.27, issue.10, pp.50-744, 2008.
DOI : 10.1111/j.1469-8749.2008.03089.x

J. Park and I. W. Sandberg, Approximation and Radial-Basis-Function Networks, Neural Computation, vol.2, issue.2, pp.305-316, 1993.
DOI : 10.1162/neco.1991.3.2.246

W. J. Peacock, The Pathophysiology of Spasticity The Identification and Treatment of Gait Problems in Cerebral Palsy, )., number 180-181 in Clinics in Developmental Medicine, pp.89-98, 2009.

J. Perry and J. Burnfield, Gait Analysis, Journal of Pediatric Orthopaedics, vol.12, issue.6, 2010.
DOI : 10.1097/01241398-199211000-00023

R. Polikar, Ensemble based systems in decision making. Circuits and Systems Magazine, IEEE, vol.6, issue.3, pp.21-45, 2006.

T. E. Raghunathan, J. M. Lepkowski, J. Van-hoewyk, and P. Solenberger, A multivariate technique for multiply imputing missing values using a sequence of regression models, Survey methodology, vol.27, issue.1, pp.85-96, 2001.

H. K. Ramakrishnan and M. P. Kadaba, On the estimation of joint kinematics during gait, Journal of Biomechanics, vol.24, issue.10, pp.969-977, 1991.
DOI : 10.1016/0021-9290(91)90175-M

M. P. Rani and G. Arumugam, An efficient gait recognition system for human identification using modified ICA, International journal of computer science and information technology, vol.2, issue.1, pp.55-67, 2010.

J. M. Rodda, H. K. Graham, L. Carson, M. P. Galea, and R. Wolfe, Sagittal gait patterns in spastic diplegia, The Journal of Bone and Joint Surgery. British volume, vol.86, issue.2, pp.86-251, 2004.
DOI : 10.1302/0301-620X.86B2.13878

P. Rosenbaum, N. Paneth, A. Leviton, M. Goldstein, M. Bax et al., A report: the definition and classification of cerebral palsy, Developmental Medicine and Child Neurology. Supplement, vol.109, pp.8-14, 2006.

F. Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the brain., Psychological Review, vol.65, issue.6, pp.386-408, 1958.
DOI : 10.1037/h0042519

P. J. Rousseeuw and K. Van-driessen, Computing LTS Regression for Large Data Sets, Data Mining and Knowledge Discovery, vol.12, issue.1, pp.29-45, 2006.
DOI : 10.1007/s10618-005-0024-4

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by backpropagating errors, Nature, issue.6088, pp.323-533, 1986.
DOI : 10.1038/323533a0

E. Rutz, S. Donath, O. Tirosh, H. K. Graham, and R. Baker, Explaining the variability improvements in gait quality as a result of single event multi-level surgery in cerebral palsy, Gait & Posture, vol.38, issue.3, pp.455-460, 2013.
DOI : 10.1016/j.gaitpost.2013.01.014

I. C. Sacco, E. Y. Suda, V. Vigneron, and C. D. Sartor, An 'Importance' Map of Signs and Symptoms to Classify Diabetic Polyneuropathy: An Exploratory Data Analysis, PLOS ONE, vol.42, issue.1, 2015.
DOI : 10.1371/journal.pone.0129763.t003
URL : https://hal.archives-ouvertes.fr/hal-01181183

R. Sameni, Extraction of fetal cardiac signals from an array of maternal abdominal recordings, 2008.
URL : https://hal.archives-ouvertes.fr/tel-00373361

A. P. Santos, F. Benamar, P. Bidaud, and E. Desailly, Dynamic simulation of different multi-contact gait patterns identified in patients affected by cerebral plasy, Gait & Posture, vol.49, p.81, 2016.

R. E. Schapire, The strength of weak learnability, Machine learning, vol.5, issue.2, pp.197-227, 1990.

V. A. Scholtes, J. G. Becher, A. Beelen, and G. J. Lankhorst, Clinical assessment of spasticity in children with cerebral palsy: a critical review of available instruments, Developmental Medicine & Child Neurology, vol.91, issue.01, pp.64-73, 2006.
DOI : 10.1111/j.1469-8749.2003.tb00418.x

L. M. Schutte, U. Narayanan, J. L. Stout, P. Selber, J. R. Gage et al., An index for quantifying deviations from normal gait, Gait & Posture, vol.11, issue.1, pp.25-31, 2000.
DOI : 10.1016/S0966-6362(99)00047-8

M. H. Schwartz, T. F. Novacheck, and J. Trost, A tool for quantifying hip flexor function during gait, Gait & Posture, vol.12, issue.2, pp.122-127, 2000.
DOI : 10.1016/S0966-6362(00)00064-3

M. H. Schwartz and A. Rozumalski, The gait deviation index: A new comprehensive index of gait pathology, Gait & Posture, vol.28, issue.3, pp.351-357, 2008.
DOI : 10.1016/j.gaitpost.2008.05.001

K. Schweizer, P. C. Cattin, R. Brunner, B. Müller, C. Huber et al., Automatic selection of a representative trial from multiple measurements using Principle Component Analysis, Journal of Biomechanics, vol.45, issue.13, pp.45-2306, 2012.
DOI : 10.1016/j.jbiomech.2012.06.012

A. Sebsadji, Evaluation d'un outil d'aide à la décision chirurgicale. Contribution des techniques de classification supervisée, 2011.

R. Shapiro, Direct linear transformation method for three-dimensional cinematography, Research Quarterly, vol.49, issue.2, pp.197-205, 1978.

L. Sirovich and M. Kirby, Low-dimensional procedure for the characterization of human faces, Journal of the Optical Society of America A, vol.4, issue.3, pp.519-524, 1987.
DOI : 10.1364/JOSAA.4.000519

S. Sohrweide, Foot Biomechanics and Pathology The Identification and Treatment of Gait Problems in Cerebral Palsy, )., number 180-181 in Clinics in Developmental Medicine, pp.205-221, 2009.

E. Stühler and D. Merhof, Principal Component Analysis Applied to SPECT and PET Data of Dementia Patients ??? A Review, Principal Component Analysis -Multidisciplinary Applications. Bibliothek der, 2012.
DOI : 10.5772/38010

H. Stoppiglia, G. Dreyfus, R. Dubois, and Y. Oussar, Ranking a random feature for variable and feature selection, The Journal of Machine Learning Research, vol.3, pp.1399-1414, 2003.

J. L. Stout, T. F. Novacheck, J. R. Gage, and M. H. Schwartz, Treatment of Crouch Gait The Identification and Treatment of Gait Problems in Cerebral Palsy, Clinics in Developmental Medicine, pp.180-181, 2009.

. Stuart, Kendall's Advanced Theory of Statistics: Distribution Theory, 1994.

F. Stulp and O. Sigaud, Many regression algorithms, one unified model: A review, Neural Networks, vol.69, pp.60-79, 2015.
DOI : 10.1016/j.neunet.2015.05.005
URL : https://hal.archives-ouvertes.fr/hal-01162281

D. H. Sutherland and J. R. Davids, Common gait abnormalities of the knee in cerebral palsy, Clinical Orthopaedics and Related Research, issue.288, pp.139-147, 1993.

G. W. Taylor, Composable, distributed-state models for high-dimensional time series, 2009.

G. W. Taylor, G. E. Hinton, and S. T. Roweis, Modeling human motion using binary latent variables, Advances in neural information processing systems, pp.1345-1352, 2006.

M. E. Tipping and C. M. Bishop, Probabilistic Principal Component Analysis, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.61, issue.3, pp.61-611, 1999.
DOI : 10.1111/1467-9868.00196

I. Tosic and P. Frossard, Dictionary Learning, IEEE Signal Processing Magazine, vol.28, issue.2, pp.27-38, 2011.
DOI : 10.1109/MSP.2010.939537

J. P. Trost, Clinical Assessment The Identification and Treatment of Gait Problems in Cerebral Palsy, )., number 180-181 in Clinics in Developmental Medicine, pp.181-204, 2009.

S. Tufféry, Data mining et statistique décisionnelle, 2012.

M. A. Turk and A. P. Pentland, Face recognition using eigenfaces, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.586-591, 1991.
DOI : 10.1109/CVPR.1991.139758

S. Õunpuu, P. Thomason, A. Harvey, and H. K. Graham, Classification of Cerebral Palsy and Patterns of Gait Pathology The Identification and Treatment of Gait Problems in Cerebral Palsy, Clinics in Developmental Medicine, pp.180-181, 2009.

V. Vigneron, Méthodes d'apprentissage statistiques et problèmes inverses Application à la spectrographie, 1997.

V. Vigneron, A. Kodewitz, A. Tome, S. Lelandais, and E. Lang, Alzheimer's Disease Brain Areas: The Machine Learning Support for Blind Localization, Current Alzheimer Research, vol.13, issue.5, pp.498-508, 2016.
DOI : 10.2174/1567205013666160314144822

S. Wang, Classification with incomplete survey data: a Hopfield neural network approach, Computers & Operations Research, vol.32, issue.10, pp.2583-2594, 2005.
DOI : 10.1016/j.cor.2004.03.018

P. J. Werbos, Beyond regression: new tools for prediction and analysis in the behavioral sciences, 1974.

T. J. Winters, J. R. Gage, and R. Hicks, Gait patterns in spastic hemiplegia in children and young adults, Journal of Bone and Joint Surgery, vol.69, issue.3, pp.437-441, 1987.

D. H. Wolpert, Stacked generalization, Neural Networks, vol.5, issue.2, pp.241-259, 1992.
DOI : 10.1016/S0893-6080(05)80023-1

T. A. Wren, I. Gorton, G. E. Õunpuu, S. Tucker, and C. A. , Efficacy of clinical gait analysis: A systematic review, Gait & Posture, vol.34, issue.2, pp.149-153, 2011.
DOI : 10.1016/j.gaitpost.2011.03.027

M. Yeargin-allsopp, K. Van-naarden-braun, N. S. Doernberg, R. E. Benedict, R. S. Kirby et al., Prevalence of Cerebral Palsy in 8-Year-Old Children in Three Areas of the United States in 2002: A Multisite Collaboration, PEDIATRICS, vol.121, issue.3, pp.121-547, 2008.
DOI : 10.1542/peds.2007-1270

V. J. Yohai, High breakdown-point and high efficiency robust estimates for regression. The Annals of Statistics, pp.642-665, 1987.

P. List, C. Galarraga, O. Vigneron, V. Dorizzi, B. Khouri et al., Predicting surgery effect on knee kinematics in cerebral palsy, Gait & Posture, issue.S1, pp.42-44, 2015.

C. Galarraga, O. A. Khouri, N. Vigneron, V. Dorizzi, B. Desailly et al., Predicting kinematic outcome of multi-level surgery in cerebral palsy, Gait & Posture, issue.S2, p.491, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01453117

C. Galarraga, O. A. Vigneron, V. Dorizzi, B. Khouri, N. Desailly et al., Estimation of Postoperative Knee Flexion at Initial Contact of Cerebral Palsy Children using Neural Networks, Proceedings of the International Conference on Pattern Recognition Applications and Methods, pp.338-342, 2015.
DOI : 10.5220/0005286503380342
URL : https://hal.archives-ouvertes.fr/hal-01187044

C. Galarraga, O. A. Vigneron, V. Dorizzi, B. Khouri, N. Desailly et al., Predicting postoperative knee flexion during gait of cerebral palsy children, Computer Methods in Biomechanics and Biomedical Engineering, vol.18, issue.sup1, pp.181940-1941, 2015.
DOI : 10.1016/j.csda.2011.04.012
URL : https://hal.archives-ouvertes.fr/hal-01187018

C. Galarraga, O. A. Vigneron, V. Dorizzi, B. Khouri, N. Desailly et al., Predicting postoperative knee flexion and pelvic tilt at initial contact of cerebral palsy children, Movement & Sport Sciences - Science & Motricit??, vol.93, issue.93, pp.87-92, 2016.
DOI : 10.1097/01241398-200105000-00023
URL : https://hal.archives-ouvertes.fr/hal-01706383

C. Galarraga, O. A. Vigneron, V. Dorizzi, B. Khouri, N. Desailly et al., Predicting postoperative gait in cerebral palsy, Gait & Posture, vol.52, pp.45-51, 2017.
DOI : 10.1016/j.gaitpost.2016.11.012
URL : https://hal.archives-ouvertes.fr/hal-01409525

C. Galarraga, O. A. Vigneron, V. Dorizzi, B. Khouri, N. Desailly et al., Predicting postoperative gait in cerebral palsy using ensemble learning, 2017.