| An introduction to kernel-based learning algorithms KR Müller, S Mika, K Tsuda, K Schölkopf Handbook of neural network signal processing, 4-1-4-40, 2018 | 4910 | 2018 |
| Fisher discriminant analysis with kernels S Mika, G Ratsch, J Weston, B Scholkopf, KR Mullers Neural networks for signal processing IX: Proceedings of the 1999 IEEE …, 1999 | 4142 | 1999 |
| 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 | 1683 | 1999 |
| Kernel PCA and de-noising in feature spaces S Mika, B Schölkopf, A Smola, KR Müller, M Scholz, G Rätsch Advances in neural information processing systems 11, 1998 | 1420 | 1998 |
| A kernel view of the dimensionality reduction of manifolds J Ham, DD Lee, S Mika, B Schölkopf Proceedings of the twenty-first international conference on Machine learning, 47, 2004 | 705 | 2004 |
| Engineering support vector machine kernels that recognize translation initiation sites A Zien, G Rätsch, S Mika, B Schölkopf, T Lengauer, KR Müller Bioinformatics 16 (9), 799-807, 2000 | 652 | 2000 |
| Benchmark data set for in silico prediction of Ames mutagenicity K Hansen, S Mika, T Schroeter, A Sutter, A Ter Laak, T Steger-Hartmann, ... Journal of chemical information and modeling 49 (9), 2077-2081, 2009 | 328 | 2009 |
| Constructing boosting algorithms from SVMs: An application to one-class classification G Ratsch, S Mika, B Scholkopf, KR Muller IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (9), 1184-1199, 2002 | 325 | 2002 |
| Invariant feature extraction and classification in kernel spaces S Mika, G Rätsch, J Weston, B Schölkopf, A Smola, KR Müller Advances in neural information processing systems 12, 1999 | 250 | 1999 |
| Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces S Mika, G Ratsch, J Weston, B Scholkopf, A Smola, KR Muller IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (5), 623-628, 2003 | 249 | 2003 |
| A mathematical programming approach to the kernel fisher algorithm S Mika, G Rätsch, KR Müller Advances in neural information processing systems 13, 2000 | 246 | 2000 |
| Kernel fisher discriminants S Mika | 188 | 2003 |
| Advances in neural information processing systems S Mika, G Rätsch, B Weston, B Schölkopf, AJ Smola, K Müller Advances in neural information processing systems, 526-542, 2000 | 188 | 2000 |
| Kernel PCA Pattern Reconstruction via Approximate Pre-Images B Schölkopf, S Mika, A Smola, G Rätsch, KR Müller ICANN 98: Proceedings of the 8th International Conference on Artificial …, 1998 | 171 | 1998 |
| An improved training algorithm for kernel fisher discriminants S Mika, AJ Smola, B Schölkopf International Workshop on Artificial Intelligence and Statistics, 209-215, 2001 | 136 | 2001 |
| Classifying ‘drug-likeness' with kernel-based learning methods KR Müller, G Rätsch, S Sonnenburg, S Mika, M Grimm, N Heinrich Journal of chemical information and modeling 45 (2), 249-253, 2005 | 116 | 2005 |
| Regularized principal manifolds A Smola, S Mika, B Schölkopf, R Williamson MIT Press, 2001 | 110 | 2001 |
| Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules TS Schroeter, A Schwaighofer, S Mika, A Ter Laak, D Suelzle, U Ganzer, ... Journal of Computer-aided molecular design 21, 485-498, 2007 | 91 | 2007 |
| Accurate solubility prediction with error bars for electrolytes: A machine learning approach A Schwaighofer, T Schroeter, S Mika, J Laub, A Ter Laak, D Sülzle, ... Journal of chemical information and modeling 47 (2), 407-424, 2007 | 88 | 2007 |
| Robust ensemble learning G Rätsch, B Schölkopf, A Smola, S Mika, T Onoda, KR Muller Advances in large margin classifiers, 2000 | 68 | 2000 |
Klaus-Robert MüllerTU Berlin & Korea University & Google Deepmind & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
