Publications


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Tech reps and submitted papers

• A. Rakotomamonjy, Simultaneous Sparse Approximations : insights and algorithms,  hal-00328185, [PDF]
  
  

Journals, book chapters and patents

To appear

•  M. Szafranski, Y. Grandvalet, A. Rakotomamonjy, Composite Kernel Learning, Machine Learning Journal,  to appear, 2010. [PDF] [code]

2009

• G. Gasso, A. Rakotomamonjy, S. Canu, Recovering sparse signals with non-convex penalties and DC programming,  IEEE Trans.  Signal Processing, Vol 57, N°12, pp 4686-4698, 2009. [PDF] [erratum] [code]

• M.-H. Bekaert, C. Botte-Lecocq, F. Cabestaing, A. Rakotomamonjy, Les interfaces Cerveau-Machine pour la palliation du handicap moteur sévère, soumis à Sciences et Technologies pour le Handicap, Vol. 3, N°1, pp95-121, 2009. [PDF]
  

2008

• A. Rakotomamonjy, F. Bach, Y. Grandvalet, S. Canu, SimpleMKL,  Journal of Machine Learning Research, Vol. 9, pp 2491-2521, 2008. [JMLR page][PDF] [code]

• A. Rakotomamonjy, V. Guigue, BCI Competition III : Dataset II - Ensemble of SVMs for BCI P300 speller, IEEE Trans. Biomedical Engineering, Vol. 55, N°3, pp 1147-1154, 2008.  [PDF]

• A. Rakotomamonjy, R. Le Riche, D. Gualandris, Z. Harchaoui, A comparison of statistical learning approaches for engine torque estimation, Control Engineering Practice, Vol. 16, pp 43-55, 2008. [PDF]

2007

• A. Rakotomamonjy, Analysis of SVM regression bound for variable ranking, Neurocomputing, Vol 70, pp 1489-1491, 2007. [PDF]  [code]

2006

• V. Guigue, A. Rakotomamonjy, S. Canu, Estimation de signaux par noyaux d'ondelettes,  Traitement du Signal,  Volume 23, N°5-6, pp 448-460, 2006. [PDF]

• V. Guigue, A. Rakotomamonjy, S. Canu, Kernel Basis Pursuit,  in Revue d'Intelligence Artificielle, New methods in Machine Learning, Vol 20, N°6 , pp 757-774, 2006. [PS]

• V. Guigue, A. Rakotomamonjy, S. Canu, Translation invariant classification of non-stationary signals. Neurocomputing, Vol 69,  pp 743-753, 2006.   [PDF]

• G. Loosli, S.-G. Lee, V. Guigue , A. Rakotomamonjy, S. Canu, Perception d'états affectifs et apprentissage,  Revue d'intelligence artificielle, Edition spéciale Interactions Emotionnelles, Vol 20, Num 4-5, pp 553-582, 2006. [PDF]

2005

• A. Rakotomamonjy, S. Canu, Frame, Reproducing Kernel, Regularization and Learning, Journal of Machine Learning Research, Vol 6, pp 1485-1515, 2005. [PDF]  [code]
  

• D. Gualandris, R. Le Riche, A. Rakotomamonjy, Reconstruction d'historiques temporels du couple moteur (engine torque estimation), French patent demand INPI No. 05/03/748, April 14 2005.

• A. Rakotomamonjy, K. Gasso, S. Canu, P. Vannoorenberghe, Prévisions de concentrations d'ozone : comparaison de différentes méthodes statistiques de type boîte noire, Journal Européen de Systèmes Automatisés, Vol 39, pp 533-552, 2005. [PDF]
  

• A. Rakotomamonjy, X. Mary, S.Canu, Non Parametric regression with wavelet kernels,  Applied Stochastics Model for Business and Industry,  Vol 21, p 153-163, 2005.   [PDF]  [code]

2004 and before

• A. Rakotomamonjy, Variable Selection using SVM-based criteria, Journal of Machine Learning Research, Special Issue on Variable Selection, Vol 3, pp 1357-1370, 2003.  [PDF]   [code]   JMLR webpage

• S. Canu,  X. Mary, A. Rakotomamonjy : Functional learning through kernel, J. Suykens, G. Horvath, S. Basu, C. Micchelli, J. Vandewalle (Eds.)  Advances in Learning Theory: Methods, Models and Applications,  NATO Science Series III: Computer and Systems Sciences, Vol. 190,  pp 89-110, IOS Press, Amsterdam 2003.   [PDF]

• A. Rakotomamonjy, R. Leriche, D. Gualandris, Comparaison de stratégies de discrimination de masse de véhicules,  Revue Extraction, Connaissances et Apprentissage, Volume 6, pp 753-784, 2002. [PDF]

• A. Rakotomamonjy, P. Deforge, P. Marché , Wavelet-based speckle noise reduction in ultrasound B-scan images, Ultrasonic Imaging, Vol 22, N°2, Novembre 2000, pp 73-94.

• A. Rakotomamonjy, D. Coast, P. Marché , Wavelet-based enhancement of the signal-averaged ECG for late potentials Detection, Med.&Bio.Eng.&Comput, Vol 37, N°6, pp750-759, 1999. [PDF]
  

• A. Rakotomamonjy, B. Migeon, P. Marché, Automated neural network detection of wavelet preprocessed late potentials, Med.&Bio.Eng.&Comput, Vol 36, N°3, pp346-350, 1998.

• A. Rakotomamonjy, B. Migeon, P. Marché, Détection automatique de potentiels tardifs par la transformée de Wigner-Ville et un réseau de neurone, Innov. Tech. Biol. Med., Vol. 18, N°2, pp 87-94, 1997.

• A. Rakotomamonjy, M. Barbaud, M. Tronel, P. Marché, Système embarqué de mesure des pressions plantaires, Innov. Tech. Biol. Med., Vol. 18, N°3, pp 179-186, 1997.

•  A. Rakotomamonjy, M. Barbaud, M. Tronel, P. Marché, J-M. Rivière, Une nouvelle méthode de mesure de l’amortissement pendant la course à pied : l’analyse temps-fréquence, Cinesiologie, Vol 36, N°6, pp 1-5, 1997.

Conference proceedings

2009

• R. Flamary, A. Rakotomamonjy, G. Gasso S. Canu, Selection de variables pour l’apprentissage simultanée de tâches,  Conférence en Apprentissage, 2009

• R. Flamary, JL Rose, S. Canu, A. Rakotomamonjy, Variational Sequence Labeling, IEEE Machine Learning and Signal Processing, Grenoble, 2009 [PDF]

•  A. Rakotomamonjy, Algorithms for Multiple Basis Pursuit Denoising, Workshop on Sparse Approximation, Saint Malo, [PDF]

   
2008

• O. Chapelle, A. Rakotomamonjy,  Second order optimization of kernel parameters. In NIPS Workshop on Automatic Selection of Optimal Kernels, 2008.  [PDF]

• Y. Grandvalet, A. Rakotomamonjy, J. Keshet, S. Canu, SVM with a reject option, Advances in Neural Information Processing Systems, 2008.  [PDF]

• G. Gasso, A. Rakotomamonjy, S. Canu, Solving non-convex Lasso type problems with DC programming, IEEE Workshop in Machine Learning and Signal processing, Cancun, 2008. [PDF]

• M. Szafranski, Y. Grandvalet, A. Rakotomamonjy, Composite Kernel Learning, Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML), 2008. [PDF]

• M. Szafranski, Y. Grandvalet, A. Rakotomamonjy, Learning with group of kernels, Conférence d'Apprentissage Automatique, 2008. 

2007

• A. Rakotomamonjy, F. R. Bach, S. Canu,  Y. Grandvalet.  More Efficiency in Multiple Kernel Learning, Proceedings of the Twenty-fourth International Conference on Machine Learning (ICML), 2007. [PDF]

• A. Broggi, M. Bertozzi, M. Del Rose, M. Felisa, A. Rakotomamonjy,  F. Suard, A Pedestrian Detector Using Histograms of Oriented Gradients and a Support Vector Machine Classifier, In Procs. IEEE Intl. Conf. on Intelligent Transportation Systems 2007, Seattle, WA, USA, September 2007. [PDF]

• A. Rakotomamonjy, M. Davy, One-class SVM regularization path and comparison with alpha seeding,  Proc of 11th European Symposium on Artificial Neural Networks, Brugges, 2007. [PDF]

•  F. Suard, A. Rakotomamonjy, A. Benshrair,  Kernel on bag of paths for measuring similarity of shapes,  Proc of 11th European Symposium on Artificial Neural Networks, Brugges, 2007. [PDF]

• F. Suard, A. Rakotomamonjy, A. Benshrair,  Model selection in pedestrian detection using multiple kernel learning, IEEE Symposium on Intelligent Vehicule, Istanbul, 2007. [PDF]

• F. Suard, A. Rakotomamonjy, Mesure de similarité de graphes par noyau de sacs de chemins, 21e colloque GRETSI sur le traitement du signal et des images, Troyes, septembre 2007.

• F. Suard, A. Rakotomamonjy, Noyaux multiples : sélection de modèle pour la détection de piéton. 21e colloque GRETSI sur le traitement du signal et des images, Troyes, septembre 2007.

2006

• A. Rakotomamonjy, S. Canu, Optimisation de noyaux d'ondelettes, 8eme Conférence d' Apprentissage Automatique, 2006, pp 397-398.  [PDF]

• F. Suard, A. Rakotomamonjy, A. Benshrair,  Object Categorization using Kernels combining Graphs and Histogram of Gradients, International Conference on Image Analysis and Recognition, Lecture Notes in Computer Science Vol 4142, Spriger, pp 23-34, 2006.  [PDF]

•  F. Suard, A. Rakotomamonjy, A. Benshrair, A. Broggi, Pedestrian Detection using Infrared images and Histograms of Oriented Gradients, IEEE Symposium on Intelligent Vehicule, Tokyo, pp 206-212, 2006. [PDF]

2005

• S. Canu, V. Guigue, A. Rakotomamonjy, G. Gasso, Kernel LARS Algorithm, NIPS Workshop on accuracy-regularization frontier, 2005.

• V. Guigue, A. Rakotomamonjy, S. Canu, Kernel Basis Pursuit. Proceedings of the 16th European Conference on Machine Learning, Porto, LNAI 3720, pp 146-154,2005. [PDF]  [code]

• V. Guigue, A. Rakotomamonjy, S. Canu, Translation invariant classification of non-stationary signals. Proceedings of the 13th European Symposium on Artificial Neural Networks, Bruges, pp 473-478, 2005. [PDF]

• F. Suard, V. Guigue, A. Rakotomamonjy, A. Benshrair, Pedestrian Detection using Stereo-vision and Graph Kernels IEEE Symposium on Intelligent Vehicule, Las Vegas, 2005. [PDF]

• A. Rakotomamonjy, V. Guigue, G. Mallet, V. Alvarado, Ensemble of SVMs for improving Brain Computer Interface P300 speller performances. Proceedings of International Conference on Artificial Neural Networks, Varsovie, pp 45-50, 2005. [PDF]

• C. Delgorge, C. Rosenberger, A. Rakotomamonjy, G. Poisson, P. Vieyres , Evaluation of the Quality of Ultrasound Image Compression by Fusion of Criteria with a Support Vector Machine", Proceedings  of the Third European Signal Proceecing Conference (EUSIPCO), 2005. [PDF]

• S. Chabrier, C. Rosenberger, H. Laurent, A. Rakotomamonjy , Segmentation Evaluation Using A Support Vector Machine Proceedings of  the Third International Conference on Advances in Pattern Recognition (ICAPR), Bath, pp 426-435, 2005. [PDF]
  

• V. Guigue, A. Rakotomamonjy, S. Canu, Classification de signaux invariante en translation. 20e colloque GRETSI sur le traitement du signal et des images, Louvain, pp 949-952, 2005. [PDF]

• S. Chabrier, H. Laurent,  C. Rosenberger, A. Rakotomamonjy, Fusion de critères pour l'évaluation de résultats de segmentation d'images, 20e colloque GRETSI sur le traitement du signal et des images, Louvain, pp 917-920, 2005. [PS]

• V. Guigue, A. Rakotomamonjy, S. Canu, Estimation de signaux par noyaux d'ondelettes. 20e colloque GRETSI sur le traitement du signal et des images, Louvain, pp 763- 766, 2005. [PDF]  [code]

• F. Suard, A. Rakotomamonjy, A. Bensrhair, Détection de piétons par stéréovision et noyaux de graphes, 20e colloque GRETSI sur le traitement du signal et des images, Louvain, 683-686, 2005. [PDF]

• A. Rakotomamonjy, V. Guigue, G. Mallet, V. Alvarado, Classification d'EEG pour les interfaces cerveau-machine. 20e colloque GRETSI sur le traitement du signal et des images, Louvain, pp 719-722, 2005.  [PDF]
  

• V. Guigue, A. Rakotomamonjy, S. Canu, Kernel Basis Pursuit. 7ème Conférence francophone sur l'apprentissage automatique, Presse Universitaire de Grenoble,  pp 93-106, 2005. [PDF]  [code]

• A. Rakotomamonjy, F. Suard, Seléction de variables : application à la détection de piétons, Proceedings de Reconnaissance de Formes et Intelligence artificielles, Toulouse 2004.  [PDF]

2004

• A. Rakotomamonjy, Quadratic programming for AUC optimization, in Modelling, Computation and Optimization in Information Systems and Management Sciences, eds Le Thi Tao, Hermes Publishing, pp 603-610, 2004
  

• A. Rakotomamonjy, Optimizing AUC with Support Vector Machine (SVM), Proceedings of European Conference on Artificial Intelligence Workshop on ROC Curve and AI, Valencia, 2004. [PDF]  [code]

• C. Rosenberger, A. Rakotomamonjy, B. Emile, Generic Target Recognition, Proceedings of 12th EUSIPCO Conference, Vienna, 2004. [PDF]

2003 and before

• V. Guigue, A. Rakotomamonjy, S. Canu, Kernel Methods for emotion recognition, proceedings of GRETSI (in french),  Paris, 2003. [PDF]
  

• A. Rakotomamonjy, X. Mary, S.Canu, Wavelet Kernel and RKHS, Proc. of Statistical Learning : Theory and Applications, Paris, 2002.  [PDF]
  

• A. Rakotomamonjy, S.Canu,  Frame Kernels for Learning,  ICANN, Lecture Notes in Computer Science  2415, pp. 707-712. , 2002. [PDF]

• S. Canu, A. Rakotomamonjy, Ozone peak and pollution forecasting using Support Vectors, IFAC Workshop on environmental modelling, Yokohama, 2001. [PDF]

• A. Rakotomamonjy, Ridgelet Pursuit : Application to regression estimation, Technical Report, Perception Systèmes Information, accepted for ICANN2001. Unpublished, 2001. [PDF]

• A. Rakotomamonjy, S. Canu, Estimation de la concentration en ozone par SVM, Actes d' Automatique et Environnement 2001, Saint Etienne. [PDF]

• A. Rakotomamonjy, S. Canu , Frame, Reproducing Kernel and Learning, NIPS Workshop on New Kernel Methods for Learning, Breckenrigde CO, Dec 2000 [PS]
• A. Rakotomamonjy, P. Deforge, P. Marché, Mixture of textures : how do the texture features derived from the co-occurrence matrix vary?, SCI99/ISAS99, Orlando (FL), 1999.

• A. Rakotomamonjy, P. Marché, Wavelet-based enhancencement of lesion detectability in ultrasound B-scan images, Proceedings of the 20^th IEEE-EMBS Conference, Hong Kong, Novembre 1998.

• A. Rakotomamonjy, B. Migeon, P. Marché, Wavelet comparison for neural networks based detection of late potentials, World Congress Medical Physics & Biomedical Engineering, Med&Biol. Eng&Comp, Vol N°35, Suppl 1, p524, Nice, 1997.

• A. Rakotomamonjy, M. Barbaud, M. Tronel, P. Marché, Attenuation of the effect of skin mounting technique in tibial acceleration measurement based on the wavelet shrinkage, Proceedings ISB Footwear Group Symposium, Tokyo,p 35 1997.

• A. Rakotomamonjy, M. Barbaud, M. Tronel, P. Marché, Time-Frequency analysis of impact shock during running, Proceedings ISB Footwear Group Symposium, Tokyo, pp 14-15, 1997.

• A. Rakotomamonjy, B. Migeon, P. Marché, Late potentials recognition by using Wigner-Ville distribution and a neural network, Proceedings of 18th International Conference IEEE-EMBS, Amsterdam, CDROM ISBN 90-9010005-9,Paper N°5, 1996.

Unpublished 

• F. Suard, A. Rakotomamonjy, A. Bensrhair, Mining Shock graphs with kernels, Technical Report of LITIS AR-06-01, University of Rouen,  Dec 2006. hal-00121988, version 1 [PDF]

• A. Rakotomamonjy, SVMs and Area under ROC curves,  Technical Report PSI- INSA de Rouen, 2004.  [PDF] (the code for reproducing the paper results is now included in the toolbox). First revision for the Machine Learning Journal.  (no planned update)
  

• A. Rakotomamonjy, Variable Selection using SVM-based criteria,  Perception Systeme Information,  Insa de Rouen, Technical Report PSI 2002-04, May 2002

• [PDF]         
• Links to datasets  IDA     Jason Weston L0 norm page  
• Example of Matlab code   margin criterion