Ingo Steinwart
Title
Cited by
Cited by
Year
Support vector machines
I Steinwart, A Christmann
Springer Science & Business Media, 2008
24232008
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
6592001
A classification framework for anomaly detection
I Steinwart, D Hush, C Scovel
Journal of Machine Learning Research 6 (Feb), 211-232, 2005
3102005
Sparseness of support vector machines
I Steinwart
Journal of Machine Learning Research 4 (Nov), 1071-1105, 2003
2702003
Fast rates for support vector machines using Gaussian kernels
I Steinwart, C Scovel
Annals of Statistics 35, 575-607, 2007
2482007
Consistency of support vector machines and other regularized kernel classifiers
I Steinwart
IEEE Transactions on Information Theory 51 (1), 128-142, 2005
2432005
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
2132006
Support vector machines are universally consistent
I Steinwart
Journal of Complexity 18 (3), 768-791, 2002
1832002
Optimal Rates for Regularized Least Squares Regression.
I Steinwart, DR Hush, C Scovel
Conference on Learning Theory, 79-93, 2009
1672009
Estimating conditional quantiles with the help of the pinball loss
I Steinwart, A Christmann
Bernoulli 17 (1), 211-225, 2011
1172011
Learning from dependent observations
I Steinwart, D Hush, C Scovel
Journal of Multivariate Analysis 100 (1), 175-194, 2009
1132009
Consistency and robustness of kernel-based regression in convex risk minimization
A Christmann, I Steinwart
Bernoulli 13 (3), 799-819, 2007
1132007
Fast rates for support vector machines
C Scovel, I Steinwart
Conference on Learning Theory, 853-888, 2005
111*2005
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
1052004
How to compare different loss functions and their risks
I Steinwart
Constructive Approximation 26 (2), 225-287, 2007
1022007
Sparseness of Support Vector Machines---some asymptotically sharp bounds
I Steinwart
Advances in Neural Information Processing Systems, 1069-1076, 2004
942004
QP algorithms with guaranteed accuracy and run time for support vector machines
D Hush, P Kelly, C Scovel, I Steinwart
Journal of Machine Learning Research 7 (May), 733-769, 2006
892006
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
882012
Training SVMs without offset
I Steinwart, D Hush, C Scovel
Journal of Machine Learning Research 12 (Jan), 141-202, 2011
772011
Fast learning from non-iid observations
I Steinwart, A Christmann
Advances in neural information processing systems, 1768-1776, 2009
772009
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