| Learning with kernels: Support vector machines, regularization, optimization, and beyond B Schölkopf, AJ Smola the MIT Press, 2002 | 19754* | 2002 |
| A tutorial on support vector regression AJ Smola, B Schölkopf Statistics and computing 14 (3), 199-222, 2004 | 10734 | 2004 |
| Nonlinear component analysis as a kernel eigenvalue problem B Schölkopf, A Smola, KR Müller Neural computation 10 (5), 1299-1319, 1998 | 9194 | 1998 |
| Introduction to machine learning E Alpaydin MIT press, 2020 | 8673 | 2020 |
| 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 | 5102 | 2001 |
| Support vector regression machines H Drucker, CJ Burges, L Kaufman, A Smola, V Vapnik Advances in neural information processing systems 9, 155-161, 1996 | 3882 | 1996 |
| Support vector method for function approximation, regression estimation and signal processing V Vapnik, SE Golowich, AJ Smola Advances in neural information processing systems, 281-287, 1997 | 3429 | 1997 |
| New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000 | 3286 | 2000 |
| Advances in kernel methods: support vector learning R Soentpiet MIT press, 1999 | 3198* | 1999 |
| Support vector machines MA Hearst, ST Dumais, E Osuna, J Platt, B Scholkopf IEEE Intelligent Systems and their applications 13 (4), 18-28, 1998 | 3187 | 1998 |
| 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 | 2791 | 2016 |
| Kernel principal component analysis B Schölkopf, A Smola, KR Müller International conference on artificial neural networks, 583-588, 1997 | 2423 | 1997 |
| Principal manifolds and nonlinear dimensionality reduction via tangent space alignment Z Zhang, H Zha SIAM journal on scientific computing 26 (1), 313-338, 2004 | 2087 | 2004 |
| 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 | 2047 | 2012 |
| Kernel methods in machine learning T Hofmann, B Schölkopf, AJ Smola The annals of statistics, 1171-1220, 2008 | 1680 | 2008 |
| A generalized representer theorem B Schölkopf, R Herbrich, AJ Smola International conference on computational learning theory, 416-426, 2001 | 1543 | 2001 |
| 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 | 1540 | 1999 |
| 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 | 1532 | 2004 |
| Support vector method for novelty detection B Schölkopf, RC Williamson, AJ Smola, J Shawe-Taylor, JC Platt Advances in neural information processing systems, 582-588, 2000 | 1500 | 2000 |
| Regularization networks and support vector machines T Evgeniou, M Pontil, T Poggio Advances in computational mathematics 13 (1), 1, 2000 | 1487 | 2000 |
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and Distinguishedtuebingen.mpg.de의 이메일 확인됨
