| Scaling learning algorithms towards AI Y Bengio, Y LeCun Large-scale kernel machines 34 (5), 1-41, 2007 | 1317 | 2007 |
| Training invariant support vector machines D Decoste, B Schölkopf Machine learning 46 (1-3), 161-190, 2002 | 743 | 2002 |
| Building support vector machines with reduced classifier complexity SS Keerthi, O Chapelle, D DeCoste Journal of Machine Learning Research 7 (Jul), 1493-1515, 2006 | 358 | 2006 |
| HD-CNN: hierarchical deep convolutional neural networks for large scale visual recognition Z Yan, H Zhang, R Piramuthu, V Jagadeesh, D DeCoste, W Di, Y Yu Proceedings of the IEEE international conference on computer vision, 2740-2748, 2015 | 344 | 2015 |
| A modified finite Newton method for fast solution of large scale linear SVMs SS Keerthi, D DeCoste Journal of Machine Learning Research 6 (Mar), 341-361, 2005 | 341 | 2005 |
| Machine learning for science: state of the art and future prospects E Mjolsness, D DeCoste science 293 (5537), 2051-2055, 2001 | 300 | 2001 |
| Naïve filterbots for robust cold-start recommendations ST Park, D Pennock, O Madani, N Good, D DeCoste Proceedings of the 12th ACM SIGKDD international conference on Knowledge …, 2006 | 234 | 2006 |
| Training invariant support vector machines using selective sampling G Loosli, S Canu, L Bottou Large scale kernel machines 2, 2007 | 195 | 2007 |
| Large-scale kernel machines CJ Lin MIT press, 2007 | 191 | 2007 |
| Dynamic across-time measurement interpretation D DeCoste Artificial Intelligence 51 (1-3), 273-341, 1991 | 152 | 1991 |
| Approximation methods for Gaussian process regression J Quinonero-Candela, CE Rasmussen, CKI Williams Large-scale kernel machines, 203-223, 2007 | 151 | 2007 |
| Collaborative prediction using ensembles of maximum margin matrix factorizations D DeCoste Proceedings of the 23rd international conference on Machine learning, 249-256, 2006 | 122 | 2006 |
| Alpha seeding for support vector machines D DeCoste, K Wagstaff Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000 | 98 | 2000 |
| An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels MMS Lee, SS Keerthi, CJ Ong, D DeCoste IEEE Transactions on Neural Networks 15 (3), 750-757, 2004 | 94 | 2004 |
| Visualizing data mining models K Thearling, B Becker, D DeCoste, B Mawby, M Pilote, D Sommerfield Information visualization in data mining and knowledge discovery 24, 205-222, 2001 | 88 | 2001 |
| Compact random feature maps R Hamid, Y Xiao, A Gittens, D DeCoste International Conference on Machine Learning, 19-27, 2014 | 72 | 2014 |
| Large scale learning with string kernels S Sonnenburg, G Rätsch, K Rieck Large Scale Kernel Machines, 73-103, 2007 | 65 | 2007 |
| Ensembles of nearest neighbor forecasts D Yankov, D DeCoste, E Keogh European conference on machine learning, 545-556, 2006 | 46 | 2006 |
| Rover traverse science for increased mission science return R Castano, RC Anderson, T Estlin, D DeCoste, F Fisher, D Gaines, ... Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space …, 2003 | 42 | 2003 |
| Visualizing Mercer kernel feature spaces via kernelized locally-linear embeddings D DeCoste | 40 | 2001 |
Olivier ChapelleGoogleVerified email at chapelle.cc
