Ingo Steinwart

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Using support vector machines for anomalous change detection.

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[DOI]

James Theiler

Daniel Llamocca

Proceedings of the IEEE International Geoscience & Remote Sensing Symposium, 2010

2009

Learning from dependent observations.

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J. Multivar. Anal., 2009

Oracle inequalities for support vector machines that are based on random entropy numbers.

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J. Complex., 2009

Fast Learning from Non-i.i.d. Observations.

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Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Optimal Rates for Regularized Least Squares Regression.

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Proceedings of the COLT 2009, 2009

2008

Sparsity of SVMs that use the epsilon-insensitive loss.

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[DOI]

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Support Vector Machines.

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Information science and statistics, Springer, ISBN: 978-0-387-77241-7, 2008

2007

Stability of Unstable Learning Algorithms.

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Mach. Learn., 2007

Robust learning from bites for data mining.

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Mia Hubert

Comput. Stat. Data Anal., 2007

How SVMs can estimate quantiles and the median.

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[DOI]

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Gaps in Support Vector Optimization.

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Proceedings of the Learning Theory, 20th Annual Conference on Learning Theory, 2007

2006

An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels.

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[DOI]

IEEE Trans. Inf. Theory, 2006

QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines.

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J. Mach. Learn. Res., 2006

An Oracle Inequality for Clipped Regularized Risk Minimizers.

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Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Function Classes That Approximate the Bayes Risk.

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[DOI]

Proceedings of the Learning Theory, 19th Annual Conference on Learning Theory, 2006

2005

Consistency of support vector machines and other regularized kernel classifiers.

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[DOI]

IEEE Trans. Inf. Theory, 2005

A Classification Framework for Anomaly Detection.

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[DOI]

J. Mach. Learn. Res., 2005

Fast Rates for Support Vector Machines.

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[DOI]

Proceedings of the Learning Theory, 18th Annual Conference on Learning Theory, 2005

2004

On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition.

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J. Mach. Learn. Res., 2004

Entropy of convex hulls--some Lorentz norm results.

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J. Approx. Theory, 2004

Fast Rates to Bayes for Kernel Machines.

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Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Density Level Detection is Classification.

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Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

2003

On the Optimal Parameter Choice for v-Support Vector Machines.

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[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2003

Sparseness of Support Vector Machines.

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[DOI]

J. Mach. Learn. Res., 2003

Sparseness of Support Vector Machines---Some Asymptotically Sharp Bounds.

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Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

2002

Support Vector Machines are Universally Consistent.

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J. Complex., 2002

2001

On the Influence of the Kernel on the Consistency of Support Vector Machines.

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