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2020 – today
- 2024
[c66]Jordan Patracone, Paul Viallard, Emilie Morvant, Gilles Gasso, Amaury Habrard, Stéphane Canu:
A Theoretically Grounded Extension of Universal Attacks from the Attacker's Viewpoint. ECML/PKDD (4) 2024: 283-300- [c65]Jordan Patracone, Lucas Anquetil, Yuan Liu, Gilles Gasso, Stéphane Canu:
Linear Modeling of the Adversarial Noise Space. ECML/PKDD (4) 2024: 301-317 - 2023
- [j37]Carole Le Guyader
, Samia Ainouz, Stéphane Canu:
A Physically Admissible Stokes Vector Reconstruction in Linear Polarimetric Imaging. J. Math. Imaging Vis. 65(4): 592-617 (2023) - [c64]Julien Denize, Jaonary Rabarisoa, Astrid Orcesi, Romain Hérault, Stéphane Canu:
Similarity Contrastive Estimation for Self-Supervised Soft Contrastive Learning. WACV 2023: 2705-2715 - [e6]Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part I. Lecture Notes in Computer Science 13713, Springer 2023, ISBN 978-3-031-26386-6 [contents] - [e5]Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part II. Lecture Notes in Computer Science 13714, Springer 2023, ISBN 978-3-031-26389-7 [contents] - [e4]Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part III. Lecture Notes in Computer Science 13715, Springer 2023, ISBN 978-3-031-26408-5 [contents] - [e3]Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part IV. Lecture Notes in Computer Science 13716, Springer 2023, ISBN 978-3-031-26411-5 [contents] - [e2]Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part V. Lecture Notes in Computer Science 13717, Springer 2023, ISBN 978-3-031-26418-4 [contents] - [e1]Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part VI. Lecture Notes in Computer Science 13718, Springer 2023, ISBN 978-3-031-26421-4 [contents] - 2022
- [j36]Cyprien Ruffino, Rachel Blin, Samia Ainouz, Gilles Gasso, Romain Hérault, Fabrice Mériaudeau, Stéphane Canu:
Physically-admissible polarimetric data augmentation for road-scene analysis. Comput. Vis. Image Underst. 222: 103495 (2022) - [j35]Fabian Dubourvieux, Romaric Audigier, Angélique Loesch, Samia Ainouz, Stéphane Canu:
A formal approach to good practices in Pseudo-Labeling for Unsupervised Domain Adaptive Re-Identification. Comput. Vis. Image Underst. 223: 103527 (2022) - [j34]Tongxue Zhou, Su Ruan, Pierre Vera, Stéphane Canu:
A Tri-Attention fusion guided multi-modal segmentation network. Pattern Recognit. 124: 108417 (2022) - [j33]Tongxue Zhou, Pierre Vera, Stéphane Canu, Su Ruan:
Missing Data Imputation via Conditional Generator and Correlation Learning for Multimodal Brain Tumor Segmentation. Pattern Recognit. Lett. 158: 125-132 (2022) - [j32]Rachel Blin, Samia Ainouz, Stéphane Canu, Fabrice Mériaudeau:
The PolarLITIS Dataset: Road Scenes Under Fog. IEEE Trans. Intell. Transp. Syst. 23(8): 10753-10762 (2022) - [c63]Rachel Blin, Samia Ainouz, Stéphane Canu, Fabrice Mériaudeau:
Road Scene Analysis: A Study of Polarimetric and Color-based Features under Various Adverse Weather Conditions. VISIGRAPP (4: VISAPP) 2022: 236-244 - [i25]Cyprien Ruffino, Rachel Blin, Samia Ainouz, Gilles Gasso, Romain Hérault, Fabrice Mériaudeau, Stéphane Canu:
Physically-admissible polarimetric data augmentation for road-scene analysis. CoRR abs/2206.07431 (2022) - 2021
- [j31]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) - [j30]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) - [j29]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) - [j28]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) - [j27]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) - [j26]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) - [c62]Rachel Blin, Samia Ainouz, Stéphane Canu, Fabrice Mériaudeau:
Multimodal Polarimetric And Color Fusion For Road Scene Analysis In Adverse Weather Conditions. ICIP 2021: 3338-3342 - [c61]Ismaïla Seck, Gaëlle Loosli, Stéphane Canu:
Linear Program Powered Attack. IJCNN 2021: 1-8 - [c60]Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
A Dual Supervision Guided Attentional Network for Multimodal MR Brain Tumor Segmentation. MICAD 2021: 3-11 - [i24]Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint. CoRR abs/2102.03111 (2021) - [i23]Yi-Shuai Niu, Wentao Ding, Junpeng Hu, Wenxu Xu, Stéphane Canu:
Spatio-Temporal Neural Network for Fitting and Forecasting COVID-19. CoRR abs/2103.11860 (2021) - [i22]Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities. CoRR abs/2104.06231 (2021) - [i21]Fabian Dubourvieux, Angélique Loesch, Romaric Audigier, Samia Ainouz, Stéphane Canu:
Improving Unsupervised Domain Adaptive Re-Identification via Source-Guided Selection of Pseudo-Labeling Hyperparameters. CoRR abs/2110.07897 (2021) - [i20]Tongxue Zhou, Su Ruan, Pierre Vera, Stéphane Canu:
A Tri-attention Fusion Guided Multi-modal Segmentation Network. CoRR abs/2111.01623 (2021) - [i19]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. CoRR abs/2111.04735 (2021) - [i18]Julien Denize, Jaonary Rabarisoa, Astrid Orcesi, Romain Hérault, Stéphane Canu:
Similarity Contrastive Estimation for Self-Supervised Soft Contrastive Learning. CoRR abs/2111.14585 (2021) - [i17]Fabian Dubourvieux, Romaric Audigier, Angélique Loesch, Samia Ainouz, Stéphane Canu:
A formal approach to good practices in Pseudo-Labeling for Unsupervised Domain Adaptive Re-Identification. CoRR abs/2112.12887 (2021) - 2020
- [j25]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) - [j24]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) - [c59]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 - [c58]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 - [c57]Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint. ICPR 2020: 10243-10250 - [c56]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 - [c55]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 - [c54]Mahdi Jammal, Stéphane Canu, Maher Abdallah:
Robust and Sparse Support Vector Machines via Mixed Integer Programming. LOD (2) 2020: 572-585 - [c53]Mahdi Jammal, Stéphane Canu, Maher Abdallah:
ℓ 1 Regularized Robust and Sparse Linear Modeling Using Discrete Optimization. LOD (2) 2020: 645-661 - [c52]Guillaume Lorre, Jaonary Rabarisoa, Astrid Orcesi, Samia Ainouz, Stéphane Canu:
Temporal Contrastive Pretraining for Video Action Recognition. WACV 2020: 651-659 - [i16]Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
Brain tumor segmentation with missing modalities via latent multi-source correlation representation. CoRR abs/2003.08870 (2020) - [i15]Tongxue Zhou, Stéphane Canu, Su Ruan:
An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism. CoRR abs/2004.06673 (2020) - [i14]Tongxue Zhou, Su Ruan, Stéphane Canu:
A review: Deep learning for medical image segmentation using multi-modality fusion. CoRR abs/2004.10664 (2020) - [i13]Fabian Dubourvieux, Romaric Audigier, Angelique Loesch, Samia Ainouz, Stéphane Canu:
Unsupervised Domain Adaptation for Person Re-Identification through Source-Guided Pseudo-Labeling. CoRR abs/2009.09445 (2020)
2010 – 2019
- 2019
- [j23]Tongxue Zhou, Su Ruan, Stéphane Canu:
A review: Deep learning for medical image segmentation using multi-modality fusion. Array 3-4: 100004 (2019) - [j22]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) - [c51]Ismaïla Seck, Gaëlle Loosli, Stéphane Canu:
L1-norm double backpropagation adversarial defense. ESANN 2019 - [c50]Rachel Blin, Samia Ainouz, Stéphane Canu, Fabrice Mériaudeau:
Road scenes analysis in adverse weather conditions by polarization-encoded images and adapted deep learning. ITSC 2019: 27-32 - [c49]Emeric Dynomant, Romain Lelong, Badisse Dahamna, Clément Massonnaud, Gaétan Kerdelhué, Julien Grosjean, Stéphane Canu, Stéfan Jacques Darmoni:
Word Embedding for French Natural Language in Healthcare: A Comparative Study. MedInfo 2019: 118-122 - [c48]Tongxue Zhou, Su Ruan, Haigen Hu, Stéphane Canu:
Deep Learning Model Integrating Dilated Convolution and Deep Supervision for Brain Tumor Segmentation in Multi-parametric MRI. MLMI@MICCAI 2019: 574-582 - [c47]Quentin Debard, Jilles Steeve Dibangoye, Stéphane Canu, Christian Wolf:
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-agent Reinforcement Learning. ECML/PKDD (3) 2019: 35-52 - [i12]Ismaïla Seck, Gaëlle Loosli, Stéphane Canu:
L 1-norm double backpropagation adversarial defense. CoRR abs/1903.01715 (2019) - [i11]Quentin Debard, Jilles Steeve Dibangoye, Stéphane Canu, Christian Wolf:
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning. CoRR abs/1904.07802 (2019) - [i10]Jorge Guevara, Roberto Hirata Jr., Stéphane Canu:
Kernels on fuzzy sets: an overview. CoRR abs/1907.12991 (2019) - [i9]Rachel Blin, Samia Ainouz, Stéphane Canu, Fabrice Mériaudeau:
Road scenes analysis in adverse weather conditions by polarization-encoded images and adapted deep learning. CoRR abs/1910.04870 (2019) - [i8]Emeric Dynomant, Stéfan Jacques Darmoni, Émeline Lejeune, Gaétan Kerdelhué, Jean-Philippe Leroy, Vincent Lequertier, Stéphane Canu, Julien Grosjean:
Doc2Vec on the PubMed corpus: study of a new approach to generate related articles. CoRR abs/1911.11698 (2019) - 2018
- [c46]Quentin Debard, Christian Wolf, Stéphane Canu, Julien Arné:
Learning to Recognize Touch Gestures: Recurrent vs. Convolutional Features and Dynamic Sampling. FG 2018: 114-121 - [c45]Yuan Liu, Stéphane Canu, Paul Honeine, Su Ruan:
K-SVD with a Real ℓ0 Optimization: Application to Image Denoising. MLSP 2018: 1-6 - [i7]Quentin Debard, Christian Wolf, Stéphane Canu, Julien Arné:
Learning to recognize touch gestures: recurrent vs. convolutional features and dynamic sampling. CoRR abs/1802.09901 (2018) - 2017
- [j21]Meriem El Azami, Carole Lartizien, Stéphane Canu:
Converting SVDD scores into probability estimates: Application to outlier detection. Neurocomputing 268: 64-75 (2017) - [j20]Stéphane Canu, Dominique Fourdrinier:
Unbiased risk estimates for matrix estimation in the elliptical case. J. Multivar. Anal. 158: 60-72 (2017) - [c44]Jorge Guevara, Roberto Hirata, Stéphane Canu:
Cross product kernels for fuzzy set similarity. FUZZ-IEEE 2017: 1-6 - [c43]Ruobing Shen, Gerhard Reinelt, Stéphane Canu:
A First Derivative Potts Model for Segmentation and Denoising Using ILP. OR 2017: 53-59 - [c42]Stéphane Canu:
Machine Learning, deep learning and optimization in computer vision. QCAV 2017: 103380N - [i6]Yuan Liu, Stéphane Canu, Paul Honeine, Su Ruan:
Une véritable approche $\ell_0$ pour l'apprentissage de dictionnaire. CoRR abs/1709.05937 (2017) - [i5]Ruobing Shen, Gerhard Reinelt, Stéphane Canu:
A First Derivative Potts Model for Segmentation and Denoising Using MILP. CoRR abs/1709.07212 (2017) - 2016
- [j19]Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Alain Rakotomamonjy, Julien Audiffren:
Operator-valued Kernels for Learning from Functional Response Data. J. Mach. Learn. Res. 17: 20:1-20:54 (2016) - [j18]Gaëlle Loosli, Stéphane Canu, Cheng Soon Ong:
Learning SVM in Kreĭn Spaces. IEEE Trans. Pattern Anal. Mach. Intell. 38(6): 1204-1216 (2016) - [c41]Meriem El Azami, Carole Lartizien, Stéphane Canu:
Converting SVDD scores into probability estimates. ESANN 2016 - [c40]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 - [c39]Igor dos Santos Montagner, Roberto Hirata Jr., Nina S. T. Hirata, Stéphane Canu:
Kernel Approximations for W-Operator Learning. SIBGRAPI 2016: 386-393 - 2015
- [c38]Denis Rousselle, Stéphane Canu:
Optimal transport for semi-supervised domain adaptation. ESANN 2015 - [i4]Léa Laporte, Rémi Flamary, Stéphane Canu, Sébastien Déjean, Josiane Mothe:
Non-convex Regularizations for Feature Selection in Ranking With Sparse SVM. CoRR abs/1507.00500 (2015) - [i3]Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Alain Rakotomamonjy, Julien Audiffren:
Operator-valued Kernels for Learning from Functional Response Data. CoRR abs/1510.08231 (2015) - 2014
- [j17]Emilie Niaf, Rémi Flamary, Olivier Rouvière, Carole Lartizien, Stéphane Canu:
Kernel-Based Learning From Both Qualitative and Quantitative Labels: Application to Prostate Cancer Diagnosis Based on Multiparametric MR Imaging. IEEE Trans. Image Process. 23(3): 979-991 (2014) - [j16]Léa Laporte, Rémi Flamary, Stéphane Canu, Sébastien Déjean, Josiane Mothe:
Nonconvex Regularizations for Feature Selection in Ranking With Sparse SVM. IEEE Trans. Neural Networks Learn. Syst. 25(6): 1118-1130 (2014) - [c37]Meriem El Azami, Carole Lartizien, Stéphane Canu:
Robust outlier detection with L0-SVDD. ESANN 2014 - [c36]Antoine Lachaud, Stéphane Canu, David Mercier, Frédéric Suard:
A robust regularization path for the Doubly Regularized Support Vector Machine. ESANN 2014 - [c35]Jorge Guevara, Roberto Hirata, Stéphane Canu:
Positive definite kernel functions on fuzzy sets. FUZZ-IEEE 2014: 439-446 - 2013
- [c34]Yachen Zhu, Xilan Tian, Guobing Wu, Gilles Gasso, Shangfei Wang, Stéphane Canu:
Emotional Influence on SSVEP Based BCI. ACII 2013: 859-864 - [c33]Abou Keita, Romain Hérault, Colas Calbrix, Stéphane Canu:
Detection and quantification in real-time polymerase chain reaction. ESANN 2013 - [c32]Jorge Guevara, Roberto Hirata, Stéphane Canu:
Kernel functions in Takagi-Sugeno-Kang fuzzy system with nonsingleton fuzzy input. FUZZ-IEEE 2013: 1-8 - [c31]Julien Delporte, Alexandros Karatzoglou, Tomasz Matuszczyk, Stéphane Canu:
Socially Enabled Preference Learning from Implicit Feedback Data. ECML/PKDD (2) 2013: 145-160 - [i2]Hachem Kadri, Philippe Preux, Emmanuel Duflos, Stéphane Canu:
Multiple functional regression with both discrete and continuous covariates. CoRR abs/1301.2656 (2013) - 2012
- [j15]Xilan Tian, Gilles Gasso, Stéphane Canu:
A multiple kernel framework for inductive semi-supervised SVM learning. Neurocomputing 90: 46-58 (2012) - [c30]Julien Delporte, Stéphane Canu, Alexandros Karatzoglou:
Apprentissage et Factorisation pour la Recommandation. AAFD 2012: 1-26 - 2011
- [j14]Alain Rakotomamonjy, Rémi Flamary, Gilles Gasso, Stéphane Canu:
ellp-ellq Penalty for Sparse Linear and Sparse Multiple Kernel Multitask Learning. IEEE Trans. Neural Networks 22(8): 1307-1320 (2011) - [c29]Xilan Tian, Gilles Gasso, Stéphane Canu:
A Multi-kernel Framework for Inductive Semi-supervised Learning. ESANN 2011 - [i1]Emilie Niaf, Rémi Flamary, Carole Lartizien, Stéphane Canu:
Handling uncertainties in SVM classification. CoRR abs/1106.3397 (2011) - 2010
- [c28]Stéphane Canu:
Recent Advances in Kernel Machines. CIARP 2010: 1 - [c27]Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Manuel Davy:
Nonlinear functional regression: a functional RKHS approach. AISTATS 2010: 374-380
2000 – 2009
- 2009
- [j13]Gilles Gasso, Alain Rakotomamonjy, Stéphane Canu:
Recovering sparse signals with a certain family of nonconvex penalties and DC programming. IEEE Trans. Signal Process. 57(12): 4686-4698 (2009) - 2008
- [c26]Karina Zapien Arreola, Thomas Gärtner, Gilles Gasso, Stéphane Canu:
Regularization path for Ranking SVM. ESANN 2008: 415-420 - [c25]Yves Grandvalet, Alain Rakotomamonjy, Joseph Keshet, Stéphane Canu:
Support Vector Machines with a Reject Option. NIPS 2008: 537-544 - 2007
- [j12]Gaëlle Loosli, Stéphane Canu:
Comments on the "Core Vector Machines: Fast SVM Training on Very Large Data Sets". J. Mach. Learn. Res. 8: 291-301 (2007) - [c24]Gilles Gasso, Karina Zapien Arreola, Stéphane Canu:
Computing and stopping the solution paths for $\nu$-SVR. ESANN 2007: 253-258 - [c23]Karina Zapien Arreola, Gilles Gasso, Stéphane Canu:
Estimation of tangent planes for neighborhood graph correction. ESANN 2007: 397-402 - [c22]Alain Rakotomamonjy, Francis R. Bach, Stéphane Canu, Yves Grandvalet:
More efficiency in multiple kernel learning. ICML 2007: 775-782 - [c21]Gilles Gasso, Karina Zapien Arreola, Stéphane Canu:
Sparsity regularization path for semi-supervised SVM. ICMLA 2007: 25-30 - [c20]Gaëlle Loosli, Gilles Gasso, Stéphane Canu:
Regularization Paths for nu -SVM and nu -SVR. ISNN (3) 2007: 486-496 - [c19]Gilles Gasso, Karina Zapien Arreola, Stéphane Canu:
Smoothness and sparsity tuning for Semi-Supervised SVM. NATO ASI Mining Massive Data Sets for Security 2007: 85-86 - 2006
- [j11]Stéphane Canu, Alexander J. Smola:
Kernel methods and the exponential family. Neurocomputing 69(7-9): 714-720 (2006) - [j10]Vincent Guigue, Alain Rakotomamonjy, Stéphane Canu:
Translation-invariant classification of non-stationary signals. Neurocomputing 69(7-9): 743-753 (2006) - [j9]Vincent Guigue, Alain Rakotomamonjy, Stéphane Canu:
Kernel Basis Pursuit. Rev. d'Intelligence Artif. 20(6): 757-774 (2006) - [c18]Manuel Davy, Frédéric Desobry, Stéphane Canu:
Estimation of Minimum Measure Sets in Reproducing Kernel Hilbert Spaces and Applications. ICASSP (3) 2006: 668-671 - 2005
- [j8]Alain Rakotomamonjy, Stéphane Canu:
Frames, Reproducing Kernels, Regularization and Learning. J. Mach. Learn. Res. 6: 1485-1515 (2005) - [j7]Gaëlle Loosli, Stéphane Canu, S. V. N. Vishwanathan, Alexander J. Smola, M. Chattopadhyay:
Boîte à outils SVM simple et rapide. Rev. d'Intelligence Artif. 19(4-5): 741-767 (2005) - [c17]Vincent Guigue, Alain Rakotomamonjy, Stéphane Canu:
Kernel Basis Pursuit. CAP 2005: 93-106 - [c16]Gaëlle Loosli, Sans-Goog Lee, Stéphane Canu:
Détection de contexte par l'apprentissage. CAP 2005: 111-112 - [c15]Vincent Guigue, Alain Rakotomamonjy, Stéphane Canu:
Kernel Basis Pursuit. ECML 2005: 146-157 - [c14]Stéphane Canu, Alexander J. Smola:
Kernel methods and the exponential family. ESANN 2005: 447-454 - [c13]Vincent Guigue, Alain Rakotomamonjy, Stéphane Canu:
Translation invariant classification of non-stationary signals. ESANN 2005: 473-478 - [c12]Quoc V. Le, Alexander J. Smola, Stéphane Canu:
Heteroscedastic Gaussian process regression. ICML 2005: 489-496 - [c11]Gaëlle Loosli, Sang-Goog Lee, Stéphane Canu:
Rupture detection for context aware applications. ubiPCMM 2005 - [c10]Bruno Grilhères, Christophe Beauce, Stéphane Canu, Stephan Brunessaux:
A Platform for Semantic Annotations and Ontology Population Using Conditional Random Fields. Web Intelligence 2005: 790-793 - 2004
- [j6]Mikhail F. Kanevski, Roman Parkin, Aleksey Pozdnukhov, Vadim Timonin, Michel Maignan, Vasiliy V. Demyanov, Stéphane Canu:
Environmental data mining and modeling based on machine learning algorithms and geostatistics. Environ. Model. Softw. 19(9): 845-855 (2004) - [c9]Cheng Soon Ong, Xavier Mary, Stéphane Canu, Alexander J. Smola:
Learning with non-positive kernels. ICML 2004 - 2003
- [j5]Icham Sefion, Abdel Ennaji, Marc Gailhardou, Stéphane Canu:
Aide à la décision médicale Contribution pour la prise en charge de l'asthme. Ingénierie des Systèmes d Inf. 8(1): 11-32 (2003) - [c8]Icham Sefion, Abdel Ennaji, Marc Gailhardou, Stéphane Canu:
ADEMA: A Decision Support System for Asthma Health Care. MIE 2003: 623-628 - 2002
- [j4]Alain Rakotomamonjy, Rodolphe Le Riche, David Gualandris, Stéphane Canu:
Comparaison de stratégies de discrimination de masses de véhicules automobiles. Rev. d'Intelligence Artif. 16(6): 753-784 (2002) - [c7]Alain Rakotomamonjy, Stéphane Canu:
Frame Kernels for Learning. ICANN 2002: 707-712 - [c6]Yves Grandvalet, Stéphane Canu:
Adaptive Scaling for Feature Selection in SVMs. NIPS 2002: 553-560 - 2000
- [j3]Skander Soltani, Daniel Boichu, Patrice Y. Simard, Stéphane Canu:
The long-term memory prediction by multiscale decomposition. Signal Process. 80(10): 2195-2205 (2000)
1990 – 1999
- 1998
- [c5]Yves Grandvalet, Stéphane Canu:
Outcomes of the Equivalence of Adaptive Ridge with Least Absolute Shrinkage. NIPS 1998: 445-451 - 1997
- [j2]Yves Grandvalet, Stéphane Canu, Stéphane Boucheron:
Noise Injection: Theoretical Prospects. Neural Comput. 9(5): 1093-1108 (1997) - [c4]Skander Soltani, Stéphane Canu, Daniel Boichu, Yves Grandvalet:
Wavelet Frames Based Estimator. ICANN 1997: 319-324 - [c3]Yves Grandvalet, Stéphane Canu:
Adaptive Noise Injection for Input Variables Relevance Determination. ICANN 1997: 463-468 - 1995
- [j1]Yves Grandvalet, Stéphane Canu:
Comments on "Noise injection into inputs in back propagation learning". IEEE Trans. Syst. Man Cybern. 25(4): 678-681 (1995) - [c2]Yves Grandvalet, Stéphane Canu, Stéphane Boucheron:
Control of complexity in learning with perturbed inputs. ESANN 1995 - 1993
- [c1]Thibault Langlois, Stéphane Canu:
B-Learning: A Reinforcement Learning Algorithm, Comparison with Dynamic Programming. IWANN 1993: 261-266
Coauthor Index
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