S. Canu

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Training Invariant Support Vector Machines using Selective Sampling

S. Canu, leon, 2005 .

M L ] 6 O ct 2 00 9 Functional learning through kernel

S. Canu, Xavier Mary, A. Rakotomamonjy, 2009 .

Functional learning through kernel

S. Canu, Xavier Mary, A. Rakotomamonjy, 2002 .

Kernel methods and the exponential family

Alexander J. Smola, Stéphane Canu, S. Canu, 2006, ESANN.

Multiple functional regression with both discrete and continuous covariates pdf

Stéphane Canu, Philippe Preux, Hachem Kadri, 2011, ArXiv.

Support Vector Machines with a Reject Option

Yves Grandvalet, Stéphane Canu, Alain Rakotomamonjy, 2008, NIPS.

Adaptive Scaling for Feature Selection in SVMs

Yves Grandvalet, Stéphane Canu, Yves Grandvalet, 2002, NIPS.

More efficiency in multiple kernel learning

Yves Grandvalet, Stéphane Canu, Francis R. Bach, 2007, ICML '07.

Noise Injection: Theoretical Prospects

Yves Grandvalet, Stéphane Canu, Stéphane Boucheron, 1997, Neural Computation.

Comments on the "Core Vector Machines: Fast SVM Training on Very Large Data Sets"

Stéphane Canu, Gaëlle Loosli, S. Canu, 2007, J. Mach. Learn. Res..

Heteroscedastic Gaussian process regression

Alexander J. Smola, Quoc V. Le, Stéphane Canu, 2005, ICML.

Support Measure Data Description for group anomaly detection

S. Canu, Jorge Guevara, R. Hirata, 2015, KDD 2015.

Environmental and Pollution Data Mapping with Support Vector Regression

S. Canu, M. Kanevski, 2003 .

Non‐parametric regression with wavelet kernels

S. Canu, Xavier Mary, A. Rakotomamonjy, 2005 .

SVM Multi-Task Learning and Non convex Sparsity Measure

S. Canu, A. Rakotomamonjy, Rémi Flamary, 2009 .

Proposition D'ARC INRIA MaBI : Machine Learning for Brain Computer Interfaces

Michèle Sebag, Stéphane Canu, Rémi Munos, 2009 .

Training Invariant Support Vector Machines using Selective Sampling

L. Bottou, S. Canu, leon, 2005 .

Context changes detection by one-class SVMs ?

S. Canu, Gaëlle Loosli, Sang-Goog Lee, 2005 .

S. Canu, Jorge Guevara, R. Hirata, 2015 .

Operator-Valued Kernels for Nonparametric Operator Estimation

P. Preux, S. Canu, E. Duflos, 2011 .

General Framework for Nonlinear Functional Regression with Reproducing Kernel Hilbert Spaces

M. Davy, P. Preux, S. Canu, 2009 .

Journal of Machine Learning Research X (2008) 1-34 Submitted 01/08; Revised 08/08; Published XX/XX

F. Bach, Yves Grandvalet, S. Canu, 2008 .

OZONE PEAK AND POLLUTION FORECASTING USING SUPPORT VECTORS

Stéphane Canu, Alain Rakotomamonjy, S. Canu, 2001 .

Introduction to optimization with applications in astronomy and astrophysics

S. Canu, Rémi Flamary, D. Mary, 2016 .

Learning SVM in Kreĭn Spaces

Cheng Soon Ong, Stéphane Canu, Gaëlle Loosli, 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Non positive SVM

Stéphane Canu, Gaëlle Loosli, S. Canu, 2010 .

Frame Kernels for Learning

S. Canu, A. Rakotomamonjy, 2002, ICANN.

Technical report : SVM in Krein spaces

Cheng Soon Ong, S. Canu, Gaëlle Loosli, 2013 .

New methods for the identification of a stable subspace model for dynamical systems

S. Canu, S. Canu, G. Gasso, 2008, 2008 IEEE Workshop on Machine Learning for Signal Processing.

Time series prediction and the wavelet transformS

D. Boichu, S. Canu, S. Canu, 2007 .

Akaike's Information Criterion, Cp and Estimators of Loss for Elliptically Symmetric Distributions

Stéphane Canu, Aurélie Boisbunon, Martin T. Wells, 2014 .

AIC, Cp and estimators of loss for elliptically symmetric distributions

S. Canu, M. Wells, W. Strawderman, 2013, 1308.2766.

Adaptive Noise Injection for Input Variables Relevance Determination

Yves Grandvalet, Stéphane Canu, Yves Grandvalet, 1997, ICANN.

Model Selection for Ranking SVM Using

Thomas Gärtner, Stéphane Canu, Gilles Gasso, 2009 .

Word Embedding for the French Natural Language in Health Care: Comparative Study

Stéphane Canu, Julien Grosjean, Emeric Dynomant, 2019, JMIR medical informatics.

Physically-admissible polarimetric data augmentation for road-scene analysis

S. Canu, F. Mériaudeau, G. Gasso, 2022, 2206.07431.

Smoothness and sparsity tuning for Semi-Supervised SVM

Stéphane Canu, Gilles Gasso, Karina Zapien Arreola, 2007, NATO ASI Mining Massive Data Sets for Security.

S. Canu, J. Denize, Romain H'erault, 2021, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

Functional learning through kernels

S. Canu, A. Rakotomamonjy, X. Mary, 2003, 0910.1013.

Mixed Integer Programming For Sparse Coding: Application to Image Denoising

Paul Honeine, Stéphane Canu, Yuan Liu, 2019, IEEE Transactions on Computational Imaging.

K-SVD with a Real ℓ0 Optimization: Application to Image Denoising

Paul Honeine, Stéphane Canu, Yuan Liu, 2018, 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP).

Unsupervised damage clustering in complex aeronautical composite structures monitored by Lamb waves: An inductive approach

Stéphane Canu, Nazih Mechbal, Marc Rébillat, 2021, Eng. Appl. Artif. Intell..

Kernel Basis Pursuit

Stéphane Canu, Alain Rakotomamonjy, Vincent Guigue, 2005, Rev. d'Intelligence Artif..

Frames, Reproducing Kernels, Regularization and Learning

Stéphane Canu, Alain Rakotomamonjy, S. Canu, 2005, J. Mach. Learn. Res..

A Regression Estimation Method Based on Wavelet Frames

S. Canu, S. Soltani, D. Boichu, 2022 .

Outcomes of the Equivalence of Adaptive Ridge with Least Absolute Shrinkage

Yves Grandvalet, Stéphane Canu, Yves Grandvalet, 1998, NIPS.

ℓ 1 Regularized Robust and Sparse Linear Modeling Using Discrete Optimization

Stéphane Canu, Mahdi Jammal, Maher Abdallah, 2020, LOD.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

Stéphane Canu, Mikhail F. Kanevski, Michel Maignan, 2004, Environ. Model. Softw..

Support Vector Machines for Classification and Mapping of Reservoir Data Support Vector Machines for Classification and Mapping of Reservoir Data

Stéphane Canu, Mikhail Kanevski, Michel Maignan, 2022 .

Advanced Spatial Data Analysis and Modelling with Support Vector Machines

Stéphane Canu, Alexei Pozdnoukhov, Mikhail Kanevski, 2000 .

A platform for semantic annotations and ontology population using conditional random fields

Bruno Grilhères, Stéphane Canu, Stephan Brunessaux, 2005, The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05).

Incoherent dictionary learning via mixed-integer programming and hybrid augmented Lagrangian

Paul Honeine, Su Ruan, Yuan Liu, 2020, Digit. Signal Process..

Sparsity regularization path for semi-supervised SVM

Stéphane Canu, Gilles Gasso, Karina Zapien Arreola, 2007, Sixth International Conference on Machine Learning and Applications (ICMLA 2007).

A review: Deep learning for medical image segmentation using multi-modality fusion

Su Ruan, Tongxue Zhou, Stéphane Canu, 2019, Array.

Automatic COVID‐19 CT segmentation using U‐Net integrated spatial and channel attention mechanism

Su Ruan, Tongxue Zhou, Stéphane Canu, 2020, Int. J. Imaging Syst. Technol..

An automatic COVID-19 CT segmentation based on U-Net with attention mechanism

Su Ruan, Tongxue Zhou, Stéphane Canu, 2020 .

Recovering Sparse Signals With a Certain Family of Nonconvex Penalties and DC Programming

Stéphane Canu, Alain Rakotomamonjy, Gilles Gasso, 2009, IEEE Transactions on Signal Processing.

Regularization Paths for nu -SVM and nu -SVR

Stéphane Canu, Gilles Gasso, Gaëlle Loosli, 2007, ISNN.

Software sensor design based on empirical data

Yves Grandvalet, Stéphane Canu, Marie H. Masson, 1999 .

Nonconvex Regularizations for Feature Selection in Ranking With Sparse SVM

Josiane Mothe, Léa Laporte, Rémi Flamary, 2014, IEEE Transactions on Neural Networks and Learning Systems.

Learning discontinuous piecewise affine fitting functions using mixed integer programming over lattice

Stéphane Canu, Leo Liberti, Claudia D’Ambrosio, 2021, Journal of Global Optimization.

Learning Discontinuous Piecewise Affine Fitting Functions using Mixed Integer Programming for Segmentation and Denoising

S. Canu, Leo Liberti, C. D’Ambrosio, 2020, 2001.08275.

A Physically Admissible Stokes Vector Reconstruction in Linear Polarimetric Imaging

S. Canu, Samia Ainouz, Carole Le Guyader, 2023, Journal of Mathematical Imaging and Vision.

Technology and perception: the contribution of sensory substitution systems

Stéphane Canu, P. Villon, Charles Lenay, 1997, Proceedings Second International Conference on Cognitive Technology Humanizing the Information Age.

Robust and Sparse Support Vector Machines via Mixed Integer Programming

Stéphane Canu, Mahdi Jammal, Maher Abdallah, 2020, LOD.

Operator-valued Kernels for Learning from Functional Response Data pdf

Stéphane Canu, Philippe Preux, Alain Rakotomamonjy, 2015, J. Mach. Learn. Res..

Learning with non-positive kernels

Alexander J. Smola, Cheng Soon Ong, Stéphane Canu, 2004, ICML.

Spatio-Temporal Neural Network for Fitting and Forecasting COVID-19 pdf

Yi-Shuai Niu, Stephane Canu, Wentao Ding, 2021, ArXiv.

Temporal Contrastive Pretraining for Video Action Recognition

Samia Ainouz, Jaonary Rabarisoa, Stéphane Canu, 2020, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

SVM and kernel machines

Stéphane Canu, Alain Rakotomamonjy, Gaëlle Loosli, 2011 .

Recent Advances in Kernel Machines

Stéphane Canu, S. Canu, 2010, CIARP.

The long-term memory prediction by multiscale decomposition

Stéphane Canu, Patrice Y. Simard, Skander Soltani, 2000, Signal Process..

Kernel-Based Learning From Both Qualitative and Quantitative Labels: Application to Prostate Cancer Diagnosis Based on Multiparametric MR Imaging

Carole Lartizien, Stéphane Canu, Rémi Flamary, 2014, IEEE Transactions on Image Processing.

A Tri-attention Fusion Guided Multi-modal Segmentation Network pdf

Su Ruan, Tongxue Zhou, Pierre Vera, 2021, Pattern Recognit..

Fusion based on attention mechanism and context constraint for multi-modal brain tumor segmentation

Su Ruan, Tongxue Zhou, Stéphane Canu, 2020, Comput. Medical Imaging Graph..

A Multi-Modality Fusion Network Based on Attention Mechanism for Brain Tumor Segmentation

Su Ruan, Tongxue Zhou, Stéphane Canu, 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

ADEMA: A Decision Support System for Asthma Health Care

Stéphane Canu, Abdellatif Ennaji, Icham Sefion, 2003, MIE.

Road Scene Analysis: A Study of Polarimetric and Color-based Features under Various Adverse Weather Conditions

S. Canu, F. Mériaudeau, Samia Ainouz, 2022, VISIGRAPP.

Road scenes analysis in adverse weather conditions by polarization-encoded images and adapted deep learning

Samia Ainouz, St'ephane Canu, Fabrice Meriaudeau, 2019, 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

Unsupervised Domain Adaptation for Person Re-Identification through Source-Guided Pseudo-Labeling pdf

Romaric Audigier, Samia Ainouz, Angelique Loesch, 2020, 2020 25th International Conference on Pattern Recognition (ICPR).

Latent Correlation Representation Learning for Brain Tumor Segmentation With Missing MRI Modalities

Stéphane Canu, Su Ruan, Tongxue Zhou, 2021, IEEE Transactions on Image Processing.

The PolarLITIS Dataset: Road Scenes Under Fog

Samia Ainouz, Fabrice Meriaudeau, Stephane Canu, 2021, IEEE Transactions on Intelligent Transportation Systems.

A new multimodal RGB and polarimetric image dataset for road scenes analysis

Stéphane Canu, Samia Ainouz, Fabrice Meriaudeau, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

Handling uncertainties in SVM classification

Carole Lartizien, Stéphane Canu, Rémi Flamary, 2011, 2011 IEEE Statistical Signal Processing Workshop (SSP).

Support Measure Data Description

Roberto Hirata, Jorge Guevara, S Ephane Canu, 2014 .

NEURAL NETWORK BASED MODELSFOR

S. Canu, F. Ding, 2022 .

Hysteresis phenomena between periodic and stationary solutions in a model of pacemaker and nonpacemaker coupled cardiac cells

S. Canu, J. Henry, P. Lorente, 1987, Journal of mathematical biology.

Feature-enhanced generation and multi-modality fusion based deep neural network for brain tumor segmentation with missing MR modalities pdf

Stéphane Canu, Su Ruan, Tongxue Zhou, 2021, Neurocomputing.

Invariances in Classification: an efficient SVM implementation

Alexander J. Smola, Sundar Vishwanathan, Stéphane Canu, 2005 .

Adapted learning for polarization-based car detection

Samia Ainouz, Fabrice Meriaudeau, Stéphane Canu, 2019, International Conference on Quality Control by Artificial Vision.

Estimation of Minimum Measure Sets in Reproducing Kernel Hilbert Spaces and Applications.

Stéphane Canu, Manuel Davy, Frédéric Desobry, 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism pdf

Su Ruan, St'ephane Canu, Tongxue Zhou, 2020, ArXiv.

Wavelet analysis residual kriging vs. neural network residual kriging

Vasily Demyanov, Stéphane Canu, Mikhail Kanevski, 2001 .

Converting SVDD scores into probability estimates: Application to outlier detection

Carole Lartizien, Stéphane Canu, Meriem El Azami, 2017, Neurocomputing.

Converting SVDD scores into probability estimates

Carole Lartizien, Stéphane Canu, Meriem El Azami, 2016, ESANN.

Understanding SVM (and associated kernel machines) through the development of a Matlab toolbox

Stephane Canu, S. Canu, 2014 .

Ieee Transactions on Signal Processing 1 Recovering Sparse Signals with a Certain Family of Non-convex Penalties and Dc Programming

S. Canu, A. Rakotomamonjy, G. Gasso, 2022 .

Variational sequence labeling

A. Rakotomamonjy, R. Flamary, S. Canu, 2009, 2009 IEEE International Workshop on Machine Learning for Signal Processing.

Learning to Recognize Touch Gestures: Recurrent vs. Convolutional Features and Dynamic Sampling

Christian Wolf, Stéphane Canu, Quentin Debard, 2018, 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018).

A formal approach to good practices in Pseudo-Labeling for Unsupervised Domain Adaptive Re-Identification

S. Canu, Romaric Audigier, Samia Ainouz, 2021, Comput. Vis. Image Underst..

Improving Unsupervised Domain Adaptive Re-Identification Via Source-Guided Selection of Pseudo-Labeling Hyperparameters

Romaric Audigier, Samia Ainouz, Fabian Dubourvieux, 2021, IEEE Access.

Cross product kernels for fuzzy set similarity

Stéphane Canu, Roberto Hirata, Jorge Guevara, 2017, 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

Word Embedding for French Natural Language in Healthcare: A Comparative Study

Stéphane Canu, Julien Grosjean, Emeric Dynomant, 2019, MedInfo.

$\ell_{p}-\ell_{q}$ Penalty for Sparse Linear and Sparse Multiple Kernel Multitask Learning

Stéphane Canu, Alain Rakotomamonjy, Rémi Flamary, 2011, IEEE Transactions on Neural Networks.

Doc2Vec on the PubMed corpus: study of a new approach to generate related articles pdf

Stéfan Jacques Darmoni, Stéphane Canu, Julien Grosjean, 2019, ArXiv.

Physically-admissible polarimetric data augmentation for road-scene analysis

S. Canu, F. Mériaudeau, G. Gasso, 2022, Comput. Vis. Image Underst..

Machine learning for learner English

Nicolas Ballier, Stéphane Canu, Theodora Alexopoulou, 2020, International Journal of Learner Corpus Research.

Missing Data Imputation via Conditional Generator and Correlation Learning for Multimodal Brain Tumor Segmentation

S. Canu, S. Ruan, P. Vera, 2022, Pattern Recognit. Lett..

Chapter 5. Quadratic Programming and Machine Learning — Large Scale Problems and Sparsity

Stéphane Canu, Gaëlle Loosli, S. Canu, 2010 .

Positive definite kernel functions on fuzzy sets

Stéphane Canu, Roberto Hirata, Jorge Guevara, 2014, 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

Brain tumor segmentation with missing modalities via latent multi-source correlation representation pdf

Su Ruan, Pierre Vera, Tongxue Zhou, 2020, MICCAI.

$\ell_{p}-\ell_{q}$ Penalty for Sparse Linear and Sparse Multiple Kernel Multitask Learning

S. Canu, A. Rakotomamonjy, Rémi Flamary, 2011, IEEE Transactions on Neural Networks.

Nonlinear functional regression: a functional RKHS approach

Stéphane Canu, Philippe Preux, Manuel Davy, 2010, AISTATS.

Comments on "Noise injection into inputs in back propagation learning"

Yves Grandvalet, Stéphane Canu, Yves Grandvalet, 1995, IEEE Trans. Syst. Man Cybern..

AIC and Cp as estimators of loss for spherically symmetric distributions

Stéphane Canu, Aurélie Boisbunon, Martin T. Wells, 2013 .

Criteria for variable selection with dependence

Stéphane Canu, Aurélie Boisbunon, Dominique Fourdrinier, 2011 .

Solving non-convex lasso type problems with DC programming

A. Rakotomamonjy, S. Canu, S. Canu, 2008, 2008 IEEE Workshop on Machine Learning for Signal Processing.

Regularization path for Ranking SVM

Thomas Gärtner, Stéphane Canu, Gilles Gasso, 2008, ESANN.

Word embedding for French natural language in healthcare: a comparative study (Preprint)

S. Canu, G. Kerdelhué, S. Darmoni, 2018 .

Functional learning through kernel

S. Canu, A. Rakotomamonjy, X. Mary, 2002 .

Socially Enabled Preference Learning from Implicit Feedback Data

Julien Delporte, Stéphane Canu, Alexandros Karatzoglou, 2013, ECML/PKDD.

Splines with non positive kernels

Cheng Soon Ong, S. Canu, X. Mary, 2009 .

Kernel Approximations for W-Operator Learning

Stéphane Canu, Nina Sumiko Tomita Hirata, Igor dos Santos Montagner, 2016, 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI).

Comments on the "Core Vector Machines: Fast SVM Training on Very Large Data Sets"

S. Canu, Gaëlle Loosli, 2007, J. Mach. Learn. Res..

A multiple kernel framework for inductive semi-supervised SVM learning

Stéphane Canu, Gilles Gasso, Xilan Tian, 2012, Neurocomputing.

Machine learning for real time remote detection

Bénédicte Bascle, Gilles Henaff, Benjamin Labbé, 2010, Security + Defence.

Frames , Reproducing Kernels , Regularization and Learning

S. Canu, A. Rakotomamonjy, 2005 .