| Support vector machines I Steinwart, A Christmann Springer Science & Business Media, 2008 | 2850 | 2008 |
| On the influence of the kernel on the consistency of support vector machines I Steinwart Journal of machine learning research 2 (Nov), 67-93, 2001 | 726 | 2001 |
| A Classification Framework for Anomaly Detection. I Steinwart, D Hush, C Scovel Journal of Machine Learning Research 6 (2), 2005 | 344 | 2005 |
| Sparseness of support vector machines I Steinwart Journal of Machine Learning Research 4 (Nov), 1071-1105, 2003 | 283 | 2003 |
| Fast rates for support vector machines using Gaussian kernels I Steinwart, C Scovel Annals of Statistics 35, 575-607, 2007 | 272 | 2007 |
| Consistency of support vector machines and other regularized kernel classifiers I Steinwart IEEE transactions on information theory 51 (1), 128-142, 2005 | 271 | 2005 |
| An explicit description of the reproducing kernel Hilbert spaces of Gaussian RBF kernels I Steinwart, D Hush, C Scovel IEEE Transactions on Information Theory 52 (10), 4635-4643, 2006 | 229 | 2006 |
| Optimal Rates for Regularized Least Squares Regression. I Steinwart, DR Hush, C Scovel Conference on Learning Theory, 79-93, 2009 | 208 | 2009 |
| Support vector machines are universally consistent I Steinwart Journal of Complexity 18 (3), 768-791, 2002 | 193 | 2002 |
| Estimating conditional quantiles with the help of the pinball loss I Steinwart, A Christmann Bernoulli 17 (1), 211-225, 2011 | 139 | 2011 |
| Consistency and robustness of kernel-based regression in convex risk minimization A Christmann, I Steinwart Bernoulli 13 (3), 799-819, 2007 | 129 | 2007 |
| Learning from dependent observations I Steinwart, D Hush, C Scovel Journal of Multivariate Analysis 100 (1), 175-194, 2009 | 125 | 2009 |
| How to compare different loss functions and their risks I Steinwart Constructive Approximation 26 (2), 225-287, 2007 | 124 | 2007 |
| On robustness properties of convex risk minimization methods for pattern recognition A Christmann, I Steinwart The Journal of Machine Learning Research 5, 1007-1034, 2004 | 123 | 2004 |
| Fast rates for support vector machines C Scovel, I Steinwart Conference on Learning Theory, 853-888, 2005 | 115* | 2005 |
| Mercer’s theorem on general domains: On the interaction between measures, kernels, and RKHSs I Steinwart, C Scovel Constructive Approximation 35 (3), 363-417, 2012 | 110 | 2012 |
| Sparseness of Support Vector Machines---some asymptotically sharp bounds I Steinwart Advances in Neural Information Processing Systems, 1069-1076, 2004 | 92 | 2004 |
| QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines. D Hush, P Kelly, C Scovel, I Steinwart, B Schölkopf Journal of Machine Learning Research 7 (5), 2006 | 91 | 2006 |
| Fast learning from non-iid observations I Steinwart, A Christmann Advances in neural information processing systems 22, 1768-1776, 2009 | 86 | 2009 |
| Universal kernels on non-standard input spaces A Christmann, I Steinwart Advances in neural information processing systems, 406-414, 2010 | 85 | 2010 |
Andreas ChristmannProfessor of Stochastics, University of BayreuthVerifierad e-postadress på uni-bayreuth.de
