Publications 2022
Publications 2021
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Fabian Dubourvieux, Angélique Loesch, Romaric Audigier, Samia Ainouz, Stéphane Canu:
Improving Unsupervised Domain Adaptive Re-Identification Via Source-Guided Selection of Pseudo-Labeling Hyperparameters. IEEE Access 9: 149780-149795 (2021)
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Tongxue Zhou, Su Ruan, Pierre Vera, Stéphane Canu: A Tri-Attention fusion guided multi-modal segmentation network
Pattern Recognition, 108417
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Amirhossein Rahbari, Marc Rébillat, Nazih Mechbal, Stéphane Canu:
Unsupervised damage clustering in complex aeronautical composite structures monitored by Lamb waves: An inductive approach. Eng. Appl. Artif. Intell. 97: 104099 (2021)
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Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
Feature-enhanced generation and multi-modality fusion based deep neural network for brain tumor segmentation with missing MR modalities. Neurocomputing 466: 102-112 (2021)
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Tongxue Zhou, Stéphane Canu, Su Ruan:
Automatic COVID-19 CT segmentation using U-Net integrated spatial and channel attention mechanism. Int. J. Imaging Syst. Technol. 31(1): 16-27 (2021)
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Ruobing Shen, Bo Tang, Leo Liberti, Claudia D'Ambrosio, Stéphane Canu:
Learning discontinuous piecewise affine fitting functions using mixed integer programming over lattice. J. Glob. Optim. 81(1): 85-108 (2021)
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Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
Latent Correlation Representation Learning for Brain Tumor Segmentation With Missing MRI Modalities. IEEE Trans. Image Process. 30: 4263-4274 (2021)
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3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint
T Zhou, S Canu, P Vera, S Ruan
2020 25th International Conference on Pattern Recognition (ICPR), 10243-10250
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Unsupervised domain adaptation for person re-identification through source-guided pseudo-labeling
F Dubourvieux, R Audigier, A Loesch, S Ainouz, S Canu
2020 25th International Conference on Pattern Recognition (ICPR), 4957-4964
Publications 2020
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Tongxue Zhou, Stéphane Canu, Su Ruan:
Fusion based on attention mechanism and context constraint for multi-modal brain tumor segmentation. Comput. Medical Imaging Graph. 86: 101811 (2020)
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Yuan Liu, Stéphane Canu, Paul Honeine, Su Ruan:
Incoherent dictionary learning via mixed-integer programming and hybrid augmented Lagrangian. Digit. Signal Process. 101: 102703 (2020)
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Rachel Blin, Samia Ainouz, Stéphane Canu, Fabrice Mériaudeau:
A new multimodal RGB and polarimetric image dataset for road scenes analysis. CVPR Workshops 2020: 867-876
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Fabian Dubourvieux, Romaric Audigier, Angelique Loesch, Samia Ainouz, Stéphane Canu:
Unsupervised Domain Adaptation for Person Re-Identification through Source-Guided Pseudo-Labeling. ICPR 2020: 4957-4964
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Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint. ICPR 2020: 10243-10250
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Tongxue Zhou, Su Ruan, Yu Guo, Stéphane Canu:
A Multi-Modality Fusion Network Based on Attention Mechanism for Brain Tumor Segmentation. ISBI 2020: 377-380
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Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
Brain Tumor Segmentation with Missing Modalities via Latent Multi-source Correlation Representation. MICCAI (4) 2020: 533-541
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Mahdi Jammal, Stéphane Canu, Maher Abdallah:
Robust and Sparse Support Vector Machines via Mixed Integer Programming. LOD (2) 2020: 572-585
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Mahdi Jammal, Stéphane Canu, Maher Abdallah:
ℓ 1 Regularized Robust and Sparse Linear Modeling Using Discrete Optimization. LOD (2) 2020: 645-661
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Guillaume Lorre, Jaonary Rabarisoa, Astrid Orcesi, Samia Ainouz, Stéphane Canu:
Temporal Contrastive Pretraining for Video Action Recognition. WACV 2020: 651-659
Publications 2019
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Yuan Liu, Stéphane Canu, Paul Honeine, Su Ruan:
Mixed Integer Programming For Sparse Coding: Application to Image Denoising. IEEE Trans. Computational Imaging 5(3): 354-365 (2019)
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Ismaïla Seck, Gaëlle Loosli, Stéphane Canu:
L 1-norm double backpropagation adversarial defense. CoRR abs/1903.01715 (2019)
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Quentin Debard, Jilles Steeve Dibangoye, Stéphane Canu, Christian Wolf:
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning. ECML (2019)
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Jorge Guevara, Roberto Hirata Jr., Stéphane Canu:
Kernels on fuzzy sets: an overview. CoRR abs/1907.12991 (2019)
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R Blin, S Ainouz, S Canu, F Meriaudeau,
Adapted learning for polarization-based car detection
Fourteenth International Conference on Quality Control by Artificial Vision (2019)
Publications 2018
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Quentin Debard, Christian Wolf, Stéphane Canu, Julien Arné:
Learning to Recognize Touch Gestures: Recurrent vs. Convolutional Features and Dynamic Sampling. FG 2018: 114-121
- Yuan Liu, Stéphane Canu, Paul Honeine, Su Ruan:
K-SVD with a Real ℓ0 Optimization: Application to Image Denoising. MLSP 2018: 1-6
Publications 2017
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Meriem El Azami, Carole Lartizien, Stéphane Canu:
Converting SVDD scores into probability estimates: Application to outlier detection. Neurocomputing 268: 64-75 (2017)
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Stéphane Canu, Dominique Fourdrinier:
Unbiased risk estimates for matrix estimation in the elliptical case. J. Multivariate Analysis 158: 60-72 (2017)
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Jorge Guevara, Roberto Hirata, Stéphane Canu:
Cross product kernels for fuzzy set similarity. FUZZ-IEEE 2017: 1-6
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Ruobing Shen, Gerhard Reinelt, Stéphane Canu:
A First Derivative Potts Model for Segmentation and Denoising Using ILP. OR 2017: 53-59
Publications 2016
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Meriem El Azami, Carole Lartizien, Stéphane Canu:
Converting SVDD scores into probability estimates. ESANN 2016
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Igor dos Santos Montagner, Nina Sumiko Tomita Hirata, Roberto Hirata, Stéphane Canu:
NILC: A two level learning algorithm with operator selection. ICIP 2016: 1873-1877
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Igor dos Santos Montagner, Roberto Hirata Jr., Nina S. T. Hirata, Stéphane Canu:
Kernel Approximations for W-Operator Learning. SIBGRAPI 2016: 386-393
Publications 2015
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G. Loosli, S. Canu,
C. Ong:
"SVM in Krein spaces" [PDF]
IEEE PAMI . (2015)
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H. Kadri, E. Duflos, P. Preux, S. Canu, A. Rakotomamonjy and J. Audiffren: "Operator-valued Kernels for Learning from
Functional Response Data", Accepted for publication in JMLR (2015)
Publications 2014
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A. Boisbunon, S. Canu, D. Fourdrinier, W. Strawderman, M.T. Wells:
AIC and $C_p$ as estimators of loss for elliptically symmetric
distributions" [PDF]
International Statistical Review, 82 (3), 422-439 (2014).
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L. Laporte, R. Flamary, S. Canu, S. Déjean and J. Mothe: Non-convex Regularizations for Feature Selection
in Ranking with Sparse SVM, IEEE Transactions on Neural Networks and
Learning Systems,25(6) :1118 - 1130 (2014)
- Émilie Niaf, R. Flamary, O. Rouviere, C. Lartizien and
S. Canu:
Kernel-Based Learning From Both Qualitative and Quantitative Labels: Application to Prostate Cancer Diagnosis Based on Multiparametric MR Imaging
IEEE Transactions on Image Processing,23(3) : 979-991 (2014)
Publications 2013
- Julien Delporte, Alexandros Karatzoglou, Tomasz Matuszczyk, Stéphane Canu: Socially Enabled Preference Learning from Implicit Feedback Data. ECML/PKDD (2) 2013: 145-160
Publications 2012
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Xilan Tian, Gilles Gasso, Stéphane Canu: A multiple kernel framework for inductive semi-supervised SVM learning. Neurocomputing 90: 46-58 (2012)
Publications 2011
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Emilie Niaf, Rémi Flamary, Carole Lartizien, Stéphane Canu: Handling uncertainties in SVM classification CoRR abs/1106.3397: (2011)
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Alain Rakotomamonjy, Rémi Flamary, Gilles Gasso, Stéphane Canu: ellp-ellq Penalty for Sparse Linear and Sparse Multiple Kernel Multitask Learning. IEEE Transactions on Neural Networks 22(8): 1307-1320 (2011)
- Xilan Tian, Gilles Gasso, Stéphane Canu: A Multi-kernel Framework for Inductive Semi-supervised Learning. ESANN 2011
Publications 2010
- Stéphane Canu: Recent Advances in Kernel Machines. CIARP 2010: 1
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Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Manuel Davy: Nonlinear functional regression: a functional RKHS approach. Journal of Machine Learning Research - Proceedings Track 9: 374-380 (2010)
Publications 2009
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General Framework for Nonlinear Functional Regression with Reproducing Kernel Hilbert Spaces
H Kadri, E Duflos, M Davy, P Preux, S Canu - 2009 - hal.inria.fr
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Recovering sparse signals with non-convex penalties and DC programming
G Gasso, A Rakotomamonjy, S Canu - 2009 - Accepted for publication
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Publications 2008
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Y. Grandvalet,J. Keshet, A. Rakotomamonjy, S. Canu: Suppport Vector Machines with a Reject Option Accepted at NIPS
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A. Rakotomamonjy, F. Bach, Y. Grandvalet, S. Canu: SimpleMKL, Journal of Machine Learning Research, to appear, 2008.
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G. Gasso, A. Rakotomamonjy, S. Canu: Recovering sparse signals with non-convex penalties and DC programming
Accepted at MLSP
- Karina Zapien Arreola, Thomas Gartner, Gilles Gasso, Stéphane Canu: Regularization path for Ranking SVM. ESANN 2008: 415-420
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New methods for the identification of a stable subspace model for dynamical systems
G Mallet, G Gasso, S Canu - IEEE Workshop on Machine Learning for Signal Processing, 2008
-
Smoothness and sparsity tuning for Semi-Supervised
SVM Gilles GASSO, Karma ZAPIEN and Stephane CANU, in Mining Massive Data Sets for Security - Advances in Data Mining, Search, Social Networks and Text Mining, and their Applications to Security
Edited by Francoise Fogelman-Soulié, Domenico Perrotta, Jakub Piskorski, Ralf Steinberger, 2008
Publications 2007
Stéphane Canu
MLSP Tutorial
Regularization path, sparsity and Pareto frontier in statistical learning
Slides
Alain Rakotomamonjy, Francis Bach, Stéphane Canu, Yves Grandvalet: More efficiency in multiple kernel learning. ICML 2007: 775-782
Gaëlle Loosli and Léon Bottou and Stéphane Canu
Training Invariant Support Vector Machines using Selective Sampling
in
"Large Scale Kernel Machines"
MIT Press",2007
Gaëlle Loosli and Stéphane Canu
Comments on the "Core Vector Machines: Fast SVM Training on Very Large Data Sets''
JMLR, 8(Feb):291--301, 2007
pdf
Gaëlle Loosli and Gilles Gasso Stéphane Canu
Regularization paths for nu-SVM and nu-SVR
ISNN, International Symposium on Neural Networks, 2007
Gaëlle Loosli and Stéphane Canu
Programmation quadratique et apprentissage. Grande taille et parcimonie
Optimisation en traitement du signal et de l'image",
editor "Patrick Siarry",2007
G. Gasso, K. Zapién, S. Canu.
L1-Norm Regularization Path for Sparse Semi-Supervised Laplacian SVM.
Accepted in ICMLA 2007, Cincinnati, Ohio, USA.
K. Zapién, G. Gasso, S. Canu.
Smoothness and L1-Norm Regularization Paths for Semi-Supervised Laplacian SVM.
Slovenian Workshop on Graph Theory, Bled 2007.
K. Zapién, G. Gasso, S. Canu.
Estimation of Tangent planes for Neighborhood Graph Correction,
ESANN April 2007, Brugges.
G. Gasso, K. Zapién, S. Canu.
Computing and Stopping the Solution Paths for nu-SVR,
ESANN April 2007, Brugges.
Gilles Gasso,
Karina Zapien et
Stéphane Canu,
Chemins de régularisation pour les modeles de régression de type nu-SVR,
GRETSI 2007
V. Guigue, A. Rakotomamonjy, S. Canu, Estimation de signaux par noyaux d'ondelettes. to appear in Traitement du Signal, 2007.
Stéphane CANU
Machines à noyaux pour l'apprentissage statistique
Dossier : TE5255
Date de parution : 02/2007
Techniques de l'ingénieur
Publications 2006
Stéphane Canu and Alex Smola
Kernel methods and the exponential family
Neurocomputing, Volume 69, Issues 7-9, March 2006, Pages 714-720
pdf
M. Davy, F. Desobry and S. Canu, "Estimation of Minimum Measure Sets in Reproducing Kernel Hilbert Spaces and Applications.", IEEE ICASSP 2006, Toulouse, May 2006.
pdf
V. Guigue, A. Rakotomamonjy, S. Canu, Kernel Basis Pursuit, in Revue d'Intelligence Artificielle, New methods in Machine Learning, Vol 20, N6 , pp 757-774, 2006.
V. Guigue, A. Rakotomamonjy, S. Canu, Translation invariant classification of non-stationary signals Neurocomputing, Vol 69, pp 743-753, 2006.
"G. Loosli and Sang-Goog Lee and V. Guigue and A. Rakotomamonjy and S. Canu ",
" Perception d'états affectifs et apprentissage ",
"RIA Revue d'intelligence artificielle, Edition spéciale Interactions Emotionnelles ", 2006
Gaëlle Loosli and Stéphane Canu and Léon Bottou,
SVM et apprentissage des très grandes bases de données,
CAp Conférence d'apprentissage,
2006.
Publications 2005
ESANN'05 - Kernel methods and the exponential family, Canu Stéphane and Smola Alex, National ICT Australia (Australia) pdf et les
slides
AMSDA'05 - Invariances in Classification: an efficient SVM implementation : pdf
et les
slides
CAP 2005 - Context Retrieval by Rupture Detection, Gaëlle Loosli, San-Goog Lee, Stéphane Canu pdf
UM 2005 - Context changes detection by One class SVMs, Gaëlle Loosli, San-Goog Lee, Stéphane Canu pdf
Publications 2004
G. Loosli and S.
Canu and S.V.N.
Vishwanathan and Alexander
J. Smola and Manojit Chattopadhyay,
Une boîte à outils rapide et simple pour les SVM,
CAp 2004 - Conférence d'Apprentissage,
pp 113-128,Presses Universitaires de Grenoble, isbn = 9-782706-112249 (ps
english translation : ps)
C.S. Ong, S. Canu
"Regularization by Early Stopping", Technical Report, Computer Sciences
Laboratory, RSISE, ANU, 2004. (Argues a new way to look at
regularization, which looks at filter functions on the spectrum) PS.GZ
C.S. Ong, X. Mary, S.
Canu, A.J. Smola,
"Learning with Non-Positive Kernels", In Proceedings of the 21st
International Conference on Machine Learning, 2004. pp. 639-646.
(What happens when we do not have a positive definite kernel? It turns
out that we can still do learning, in what is now a Krein Space. This
paper talks about regression with indefinite kernels.) pdf
- here is the associated poster
A. Rakotomamonjy, X. Mary, S.
Canu : "Non
Parametric regression with wavelet kernels"; Applied Stochastics Model
for Business and Industry; pp 1-18 (2004) pdf
Publications 2003
X. Mary, D. De
Brucq
et S. Canu : Sous-dualités et noyaux (reproduisants)
associés . Comptes rendus - Mathématiques.
Volume 336/11 pp. 949-954,
2003.
acces par science direct
Stéphane Canu,
Xavier
Mary and Alain
Rakotomamonjy : Functional learning through kernel. ( version postscript ) (version.pdf
)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.
Pour le projet sur l'apprentissage du contexte voir les pages de V. Guigue
et de G. Loosli
Publications 2002
Yves Grandvalet and Stéphane Canu : Adaptive
Scaling for Feature Selection in SVMs, accepted for publication
at NIPS 2002. (version
postscript , version
pdf )
Stéphane Canu, Xavier Mary and Alain
Rakotomamonjy : Functional learning through kernel.
Slides of the talk given at the NATO
Advanced
Study Institute on Learning Theory and Practice (LTP
2002)
July 11 2002 - K.U. Leuven Belgium, ( slides in pdf
)
Stéphane Canu, Alain Rakotomamonjy
et
Xavier Mary, Construction de noyaux pour les Machines
à Vecteur Support (SVM). Application aux noyaux d'ondelettes
. Exposé effectué dans le cadre de la Journée
Thématique "
Retours des sciences cognitives sur l'apprentissage automatique "
au Laboratoire Leibniz à
Grenoble le 20 Juin 2002 ( sources tex
, version
postscript , version pdf
)
S. Canu : Introduction aux SVM . Séminaire au
GREYC à Caen le 17 Janvier 2002 ( sources tex , version postscript, version pdf )
S. Canu : Modèles connexionnistes et machines à
vecteurs supports pour la décision . Chapitre de l'ouvrage "Décision
de reconnaissance des formes en Signal", éditeur R.
Lengellé, Hermès 2002.
A. Rakotomamonjy, S. Canu, Frame Kernels for Learning,
accepted for publication in ICANN 2002. (
version pdf )
A. Rakotomamonjy, X. Mary, S. Canu and D. De Brucq, Frame
Kernels for Learning, Complete version. Rapport interne PSI 02-005 ( version ps
, version
pdf )
Associated with this paper we propose an exemple of wavelet kernel: the "Daudechie"
waveletKernel (AVI)
and in 1D
and in 2d
Kanevski M., Parkin R., Pozdnukhov A., Timonin V., Maignan M.,
Yatsalo B., Canu S. (2002) Environmental Data Mining and Modelling
Based on Machine Learning Algorithms and Geostatistics.
International Environmental Modeling and Software society conference
(iEMSs2002), Lugano, Switzerland, pp. 414-419.
Kanevski M., Pozdnoukhov A., Canu S., Maignan M. (2002) Advanced
Spatial Data Analysis and Modelling with Support Vector Machines.
International Journal of Fuzzy Systems, Vol. 4, No. 1, March 2002, pp.
606-616
Publications 2001
S. Canu : Modèles connexionnistes et machines
à vecteurs supports pour la décision . Rapport
interne PSI
01-003 (version
postscript ) (bibliographie
.bbl )
S. Canu, A. Rakotomamonjy, Ozone peak and pollution forecasting
using Support Vectors, IFAC Workshop on environmental modelling,
Yokohama,
2001Yokohama, 2001. (
version pdf )
A. Rakotomamonjy, S. Canu, Estimation de la concentration en ozone
par SVM, Actes d' Automatique et Environnement 2001, Saint
Etienne. (
version pdf )
V. Demyanov, S. Soltani, M. Kanevski, S. Canu, M. Maignan, E.
Savelieva, V. Timonin, V. Pisarenko: "Wavelet analysis residual
kriging vs.
neural network residual kriging" Stochastic Environmental Research
and Risk Assessment 15 (2001) 1, 18-32. ( version pdf
)
A. Rakotomamonjy, S. Canu, Frame, Reproducing Kernel,
Regularization and Learning, Technical Report, Perception Systeme
Information, Insa de Rouen, Submitted to Journal of Learning
Machine Research, March 2001, Modification January 2002. (
version pdf )
Pozdnoukhov A., Kanevski, M., Demyanov V., Canu S., Maignan M.,
Kravetski A., Parkin R., Savelieva E., Trutce A., Chernov S. (2001)
Environmental Data Mining with Geostatistics and Machine Learning
Algorithms, 4-th INTAS Interdisciplinary Symposium on Physical and
Chemical Methods in Biology, Medicine and Environment, Moscow, May
30-June 3, 2001.
Kanevski M., Pozdnukhov A., Canu S., Maignan M., Wong P. Shibli S.
(2001) Support Vector Machines for Classification and Mapping of
Reservoir Data. A chapter from “Soft computing for reservoir
characterization and modelling”, Springer-Verlag, 2001, pp. 531-558.
Publications 2000
S. Soltani , D.
Boichu, P. Simard and S. Canu: "The long term memory
prediction by multiscale decomposition", Signal Processing, 80,
2000, pp 2195-2205 .
S. Canu, "Outils d'analyse statistique des risques : programmation
par l'exemple", exposé dans le cadre des journées
séminaire de l'objectif forage et production IFP, les 26, 27 et
28 Avril 9 2000 à Beaune (présentation
power point)
S. Canu, Ph Leray et A. Rakotomamonjy, "Une méthode de
prévision à un pas de temps : application à la
prévision de la qualié de l'air", exposé dans
le cadre des premières journées automatique et
environnement, le 9 Mars 2000 à Nancy (
présentation power point )
Publications 1999
Y. Grandvalet and C. Ambroise and S.
Canu, "Local learning by sparse radial basis functions",
ICANN99 , vol
1, pp 233-238, 1999.
S. Canu, M. Kanevski. "Pollution
Mapping with Support Vector Regression". Presented at StatGIS99
conference in Klagenfurt (Austria), 20-21 September 1999. Proceedings
will be published in the journal Mathematische Geologie.
S. Canu and A.
Elisseff, "Regularization, kernels and sigmoid nets",
Unpublished
manuscript (
postscript version of the paper )
Abstract: Many Neural networks use
sigmoid--shaped functions because of biology-inspired arguments.
But there must be some mathematical reason for their
efficiency.
This work aims at presenting such a mathematical reason. It
emerges
when the input probability distribution \mu is taken
into account in the definition of the regularization term in the
framework
of regularization theory for learning. In this case the solution of the regularized minimizing
algorithm
( i.e . the learning machine) depends on a
cumulative
distribution function, i.e. a sigmoid--shaped function.
S. Canu and A Elisseff, "pourqoi les réseaux de neurones
de type perceptron multicouche conviennent-'ils a l'appentissage".
presnetation lors des journées "Theorie de l'Apprentissage
et Modelisation
Cognitive" du Pole Rhone-Alpes à lyon les 5 et 6 mai 1999 (
presentation powerpoint )
M.H. Masson, S. Canu, Y. Grandvalet, "Software Sensor Design
based on empirical Data", Ecological Modelling, 120, 131-139, 1999
R. Le Riche, G. Cailletaud et S. Canu, "Vers un
non-linéaire matériau automatique", 4ème
Colloque en calul des Structures, Giens, France, Mai 1999
Liste des publications 1998 : Laboratoire PSI.
S. Soltani , S. Canu
and D. Boichu :"Time series prediction and the wavelet transform"
, International Workshop on Advanced Black Box modelling, Leuven,
Belgium, Jul 8-10, 1998. ( version
postscipt )
S. Canu, Y. Grandvalet
and M. H.
Masson , "Black-box Software Sensor Design for Envoronmental
Monitoring" , in International Conference on Artificial Neural
Networks , Skovde, Sweden. Sep 2-4, 1998. ( version
postscript ),
S. Soltani , S. Canu
and D. Boichu :"The wavelet transform for time series",
International Conference on Artificial Neural Networks, Skovde, Sweden.
Sep 2-4, 1998.
M. H. Masson
, S. Canu, Y. Grandvalet
and A. Lynggaard-Jensen "Software Sensor Design based on empirical
Data" , International Workshop on Applications of Neural Networks
to Ecological Modelling, Toulouse, decembre 1998. ( version
postscript ),
Tutorials :
Organisation de session :