| Learning with kernels: Support vector machines, regularization, optimization, and beyond B Schölkopf, AJ Smola the MIT Press, 2002 | 21970* | 2002 |
| A tutorial on support vector regression AJ Smola, B Schölkopf Statistics and computing 14 (3), 199-222, 2004 | 13079 | 2004 |
| Nonlinear component analysis as a kernel eigenvalue problem B Schölkopf, A Smola, KR Müller Neural computation 10 (5), 1299-1319, 1998 | 10069 | 1998 |
| Estimating the support of a high-dimensional distribution B Schölkopf, JC Platt, J Shawe-Taylor, AJ Smola, RC Williamson Neural computation 13 (7), 1443-1471, 2001 | 6113 | 2001 |
| Support vector regression machines H Drucker, CJ Burges, L Kaufman, A Smola, V Vapnik Advances in neural information processing systems 9, 1996 | 5278 | 1996 |
| Hierarchical attention networks for document classification Z Yang, D Yang, C Dyer, X He, A Smola, E Hovy Proceedings of the 2016 conference of the North American chapter of the …, 2016 | 4449 | 2016 |
| Support vector method for function approximation, regression estimation and signal processing V Vapnik, S Golowich, A Smola Advances in neural information processing systems 9, 1996 | 3796 | 1996 |
| New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000 | 3631 | 2000 |
| A kernel two-sample test A Gretton, KM Borgwardt, MJ Rasch, B Schölkopf, A Smola The Journal of Machine Learning Research 13 (1), 723-773, 2012 | 3552 | 2012 |
| Kernel principal component analysis B Schölkopf, A Smola, KR Müller International conference on artificial neural networks, 583-588, 1997 | 2882 | 1997 |
| Kernel methods in machine learning T Hofmann, B Schölkopf, AJ Smola The annals of statistics 36 (3), 1171-1220, 2008 | 2223 | 2008 |
| Support vector method for novelty detection B Schölkopf, RC Williamson, A Smola, J Shawe-Taylor, J Platt Advances in neural information processing systems 12, 1999 | 2159 | 1999 |
| Communication efficient distributed machine learning with the parameter server M Li, DG Andersen, AJ Smola, K Yu Advances in Neural Information Processing Systems 27, 2014 | 2039 | 2014 |
| A generalized representer theorem B Schölkopf, R Herbrich, AJ Smola International conference on computational learning theory, 416-426, 2001 | 1870 | 2001 |
| kernlab-an S4 package for kernel methods in R A Karatzoglou, A Smola, K Hornik, A Zeileis Journal of statistical software 11 (9), 1-20, 2004 | 1840 | 2004 |
| Stacked attention networks for image question answering Z Yang, X He, J Gao, L Deng, A Smola Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 1793 | 2016 |
| A kernel method for the two-sample-problem A Gretton, K Borgwardt, M Rasch, B Schölkopf, A Smola Advances in neural information processing systems 19, 2006 | 1769 | 2006 |
| Correcting sample selection bias by unlabeled data J Huang, A Gretton, K Borgwardt, B Schölkopf, A Smola Advances in neural information processing systems 19, 2006 | 1736 | 2006 |
| Deep sets M Zaheer, S Kottur, S Ravanbakhsh, B Poczos, RR Salakhutdinov, ... Advances in neural information processing systems 30, 2017 | 1631 | 2017 |
| Input space versus feature space in kernel-based methods B Scholkopf, S Mika, CJC Burges, P Knirsch, KR Muller, G Ratsch, ... IEEE transactions on neural networks 10 (5), 1000-1017, 1999 | 1612 | 1999 |
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and Distinguishedtuebingen.mpg.de의 이메일 확인됨
