| SimpleMKL A Rakotomamonjy, F Bach, S Canu, Y Grandvalet Journal of Machine Learning Research 9, 2491-2521, 2008 | 1850 | 2008 |
| A review: Deep learning for medical image segmentation using multi-modality fusion T Zhou, S Ruan, S Canu Array 3, 100004, 2019 | 592 | 2019 |
| Svm and kernel methods matlab toolbox S Canu http://asi. insa-rouen. fr/enseignants/~ arakoto/toolbox/index. html, 2005 | 570 | 2005 |
| More efficiency in multiple kernel learning A Rakotomamonjy, F Bach, S Canu, Y Grandvalet Proceedings of the 24th international conference on Machine learning, 775-782, 2007 | 401 | 2007 |
| Recovering sparse signals with a certain family of nonconvex penalties and DC programming G Gasso, A Rakotomamonjy, S Canu IEEE Transactions on Signal Processing 57 (12), 4686-4698, 2009 | 385 | 2009 |
| Learning with non-positive kernels CS Ong, X Mary, S Canu, AJ Smola Proceedings of the twenty-first international conference on Machine learning, 81, 2004 | 319 | 2004 |
| Adaptive scaling for feature selection in SVMs Y Grandvalet, S Canu Advances in neural information processing systems 15, 2002 | 252 | 2002 |
| Heteroscedastic Gaussian process regression QV Le, AJ Smola, S Canu Proceedings of the 22nd international conference on Machine learning, 489-496, 2005 | 251 | 2005 |
| Training invariant support vector machines using selective sampling G Loosli, S Canu, L Bottou Large scale kernel machines 2 (1), 301.320, 2007 | 247 | 2007 |
| Support vector machines with a reject option Y Grandvalet, A Rakotomamonjy, J Keshet, S Canu Advances in neural information processing systems 21, 2008 | 229 | 2008 |
| Automatic COVID‐19 CT segmentation using U‐Net integrated spatial and channel attention mechanism T Zhou, S Canu, S Ruan International Journal of Imaging Systems and Technology 31 (1), 16-27, 2021 | 196 | 2021 |
| Technology and perception: The contribution of sensory substitution systems C Lenay, S Canu, P Villon Proceedings Second International Conference on Cognitive Technology …, 1997 | 178 | 1997 |
| Environmental data mining and modeling based on machine learning algorithms and geostatistics M Kanevski, R Parkin, A Pozdnukhov, V Timonin, M Maignan, ... Environmental Modelling & Software 19 (9), 845-855, 2004 | 175 | 2004 |
| Noise injection: Theoretical prospects Y Grandvalet, S Canu, S Boucheron Neural Computation 9 (5), 1093-1108, 1997 | 166 | 1997 |
| Latent correlation representation learning for brain tumor segmentation with missing MRI modalities T Zhou, S Canu, P Vera, S Ruan IEEE Transactions on Image Processing 30, 4263-4274, 2021 | 154 | 2021 |
| Operator-valued kernels for learning from functional response data H Kadri, E Duflos, P Preux, S Canu, A Rakotomamonjy, J Audiffren Journal of Machine Learning Research 17 (20), 1-54, 2016 | 143 | 2016 |
| Kernel methods and the exponential family S Canu, A Smola Neurocomputing 69 (7-9), 714-720, 2006 | 127 | 2006 |
| Learning SVM in Kreĭn spaces G Loosli, S Canu, CS Ong IEEE transactions on pattern analysis and machine intelligence 38 (6), 1204-1216, 2015 | 126 | 2015 |
| Nonconvex regularizations for feature selection in ranking with sparse SVM L Laporte, R Flamary, S Canu, S Déjean, J Mothe IEEE Transactions on Neural Networks and Learning Systems 25 (6), 1118-1130, 2013 | 111 | 2013 |
| Frames, Reproducing Kernels, Regularization and Learning. A Rakotomamonjy, S Canu, A Smola Journal of Machine Learning Research 6 (9), 2005 | 110 | 2005 |
Alain RakotomamonjyCriteo AI Lab在 insa-rouen.fr 的电子邮件经过验证
