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Ingo Steinwart
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Support vector machines
I Steinwart, A Christmann
Springer Science & Business Media, 2008
44112008
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
9202001
A Classification Framework for Anomaly Detection.
I Steinwart, D Hush, C Scovel
Journal of Machine Learning Research 6 (2), 2005
4692005
Fast rates for support vector machines using Gaussian kernels
I Steinwart, C Scovel
Annals of Statistics 35, 575-607, 2007
3422007
Optimal Rates for Regularized Least Squares Regression.
I Steinwart, DR Hush, C Scovel
Conference on Learning Theory, 79-93, 2009
3302009
Sparseness of support vector machines
I Steinwart
Journal of Machine Learning Research 4 (Nov), 1071-1105, 2003
3132003
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
3092006
Consistency of support vector machines and other regularized kernel classifiers
I Steinwart
IEEE transactions on information theory 51 (1), 128-142, 2005
3062005
Mercer’s theorem on general domains: On the interaction between measures, kernels, and RKHSs
I Steinwart, C Scovel
Constructive Approximation 35, 363-417, 2012
2452012
Estimating conditional quantiles with the help of the pinball loss
I Steinwart, A Christmann
Bernoulli 17 (1), 211-225, 2011
2442011
Support vector machines are universally consistent
I Steinwart
Journal of Complexity 18 (3), 768-791, 2002
2192002
How to compare different loss functions and their risks
I Steinwart
Constructive Approximation 26 (2), 225-287, 2007
1992007
Consistency and robustness of kernel-based regression in convex risk minimization
A Christmann, I Steinwart
Bernoulli 13 (3), 799-819, 2007
1752007
Learning from dependent observations
I Steinwart, D Hush, C Scovel
Journal of Multivariate Analysis 100 (1), 175-194, 2009
1722009
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
1502004
Universal kernels on non-standard input spaces
A Christmann, I Steinwart
Advances in neural information processing systems 23, 2010
1332010
Sobolev norm learning rates for regularized least-squares algorithm
S Fischer, I Steinwart
Journal of Machine Learning Research 205, 1-38, 2020
1282020
Fast rates for support vector machines
C Scovel, I Steinwart
Conference on Learning Theory, 853-888, 2005
113*2005
Fast learning from non-iid observations
I Steinwart, A Christmann
Advances in neural information processing systems 22, 2009
1092009
Sparseness of support vector machines---Some asymptotically sharp bounds
I Steinwart
Advances in Neural Information Processing Systems 16, 2003
1052003
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