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Ingo Steinwart's Publications

Book

I. Steinwart and A. Christmann, Support Vector Machines. Springer, New York, 602 pages, 2008.   [   Springer Website   ]  

Submitted

I. Steinwart, D. Hush, and C. Scovel, Training SVMs without Offset. Los Alamos National Laboratory Technical Report LA-UR-09-00638. Submitted for publication, 2009.   [   Abstract   |   PDF (641 KB)   ]  

I. Steinwart and A. Christmann, Estimating Conditional Quantiles with the Help of the Pinball Loss. Los Alamos National Laboratory Technical Report LA-UR-08-4612, submitted for publication, 2008.   [   Abstract   |   PDF (169 KB)   ]  

Accepted

I. Steinwart, Two Oracle Inequalities for Regularized Boosting Classifiers. Statistics and Its Interface, to appear. Los Alamos National Laboratory Technical Report LA-UR-08-7206.   [   Abstract   |   PDF (297 KB)   ]  

I. Steinwart, Oracle inequalities for SVMs that are Based on Random Entropy Numbers. Journal of Complexity, to appear. Los Alamos National Laboratory Technical Report LA-UR-09-00637, 2009.   [   Abstract   |   PDF (237 KB)   ]  

2009

I. Steinwart, D. Hush, and C. Scovel, Learning from Dependent Observations. Journal of Multivariate Analysis, Vol. 100, pp. 175-194, 2009. Los Alamos National Laboratory Technical Report LA-UR-06-3507, 2006.   [   Abstract   |   PDF (348 KB)   ]  

I. Steinwart and M. Anghel, An SVM approach for forecasting the evolution of an unknown ergodic dynamical system from observations with unknown noise. Annals of Statistics, Vol. 37, pp. 841-875, 2009. Los Alamos National Laboratory Technical Report LA-UR-07-1829, 2007.   [   Abstract   |   PDF (325 KB)   ]  

I. Steinwart and A. Christmann, Sparsity of SVMs that use the Epsilon-Insensitive Loss. In Neural Information Processing Systems 21, pp. 1569-1576, 2009. Los Alamos National Laboratory Technical Report LA-UR-08-3631.   [   Abstract   |   PDF (202 KB)   ]  

I. Steinwart, D. Hush, and C. Scovel, Optimal Rates for Regularized Least Squares Regression. In Proceedings of the 22nd Conference on Learning Theory, 2009. Los Alamos National Laboratory Technical Report LA-UR-09-00901, 2009.   [   Abstract   |   PDF (238 KB)   ]  

2008

I. Steinwart and A. Christmann, How SVMs can Estimate Quantiles and the Median. In Neural Information Processing Systems 20, pp. 305-312, 2008. Los Alamos National Laboratory Technical Report LA-UR-07-6041, 2007.   [   Abstract   |   PDF (211 KB)   ]  

A. Christmann and I. Steinwart, Consistency of Kernel Based Quantile Regression. Applied Stochastic Models in Business and Industry, Vol. 24, pp. 171-183, 2008. Los Alamos National Laboratory Technical Report LA-UR-06-7110, 2006.   [   Abstract   |   PDF (227 KB)   ]  

2007

C. Scovel, D. Hush, and I. Steinwart, Approximate Duality. Journal of Optimization Theory and Applications, Vol. 135, pp. 429-443, 2007. Los Alamos National Laboratory Technical Report LA-UR-05-6755.   [   Abstract   |   Postscript (153 KB)   |   PDF (174 KB)   ]  

A. Christmann, I. Steinwart, and M. Hubert, Robust Learning from Bites for Data Mining. Computational Statistics & Data Analysis, Vol. 52, pp. 347-361, 2007. Los Alamos National Laboratory Technical Report LA-UR-06-2769.   [   Abstract   |   PDF (308 KB)   ]  

A. Christmann and I. Steinwart, Consistency and Robustness of Kernel Based Regression. Bernoulli, Vol. 13, pp. 799-819, 2007. Los Alamos National Laboratory Technical Report LA-UR-04-8797.   [   Abstract   |   PDF (395 KB)   ]  

I. Steinwart, D. Hush, and C. Scovel, An Oracle Inequality for Clipped Regularized Risk Minimizers. In Neural Information Processing Systems 19, pp. 1321-1328, (2007). Los Alamos National Laboratory Technical Report LA-UR-06-3981.   [   Abstract   |   PDF (121 KB)   ]  

I. Steinwart, How to compare different loss functions and their risks. Constructive Approximation, Vol. 26, pp. 225-287, 2007. Los Alamos National Laboratory Technical Report LA-UR-05-7016.   [   Abstract   |   PDF (515 KB)   ]  

I. Steinwart and C. Scovel, Fast Rates for Support Vector Machines using Gaussian Kernels. Annals of Statistics, Vol. 35, pp. 575-607, 2007. Los Alamos National Laboratory Technical Report LA-UR-04-8796.   [   Abstract   |   PDF (403 KB)   ]  

N. List, D. Hush, C. Scovel and I. Steinwart, Gaps in support vector optimization. In Proceedings of the 20th Conference on Learning Theory, 336-348, 2007. Los Alamos National Laboratory Technical Report LA-UR-07-0621.   [   Abstract   ]  

D. Hush, C. Scovel, and I. Steinwart, Stability of Unstable Learning Algorithms. Machine Learning Journal, Vol. 67, pp. 197-206, 2007. Los Alamos National Laboratory Technical Report LA-UR-03-4845, 2003.   [   Abstract   |   PostScript (413 KB)   |   PDF (322 KB)   ]  

2006

I. Steinwart, D. Hush, and C. Scovel, A new Concentration Result for Regularized Risk Minimizers. High-dimensional Probability IV, in IMS Lecture Notes--Monograph Series, Vol. 51, pp. 260-275, 2006. Los Alamos National Laboratory Technical Report LA-UR-05-9403, 2005.   [   Abstract   |   PDF (253 KB)   ]  

I. Steinwart, D. Hush, and C. Scovel Function classes that approximate the Bayes risk. In Proceedings of the 19th Conference on Learning Theory (COLT 2006), Pittsburgh, USA, 2006. Los Alamos National Laboratory Technical Report LA-UR-06-0365.   [   Abstract   ]  

D. Hush, P. Kelly, C. Scovel and I. Steinwart, QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines. Journal of Machine Learning Research, Vol. 7, pp. 733-769, 2006. Los Alamos National Laboratory Technical Report LA-UR-05-5165.   [   Abstract   |   Postscript (787 KB)   |   PDF (627 KB)   ]  

I. Steinwart, D. Hush, and C. Scovel, An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels Kernels. IEEE Transactions on Information Theory, Vol. 52, pp. 4635-4643, 2006. Los Alamos National Laboratory Technical Report LA-UR-04-8274.   [   Abstract   |   PDF (244 KB)   ]  

2005

I. Steinwart, D. Hush, and C. Scovel, A classification framework for anomaly detection. Journal of Machine Learning Research, Vol. 6, pp. 211-232, 2005. Los Alamos National Laboratory Technical Report LA-UR-04-4716.   [   Abstract   |   PostScript (535 MB)   |   PDF (243 KB)   ]  

C. Scovel, D. Hush, C. Scovel and I. Steinwart, Learning Rates for Density Level Detection. Analysis and Applications, Vol. 3, No. 4 (2005) 356-371. Los Alamos National Laboratory Technical Report LA-UR-05-2088.   [   Abstract   |   Postscript (262 KB)   |   PDF (287 KB)   ]  

C. Scovel and I. Steinwart, Fast Rates for Support Vector Machines. In Proceedings of the 18th Conference on Learning Theory (COLT 2005), Bertinoro, Italy, 2005. Los Alamos National Laboratory Technical Report LA-UR-05-0451.   [   Abstract   ]  

I. Steinwart and C. Scovel, Fast Rates to Bayes for Kernel Methods. In Neural Information Processing Systems 17, pp. 1345-1352, (2005). Los Alamos National Laboratory Technical Report LA-UR-04-3767.   [   Abstract   |   PDF (92 KB)   ]  

I. Steinwart, D. Hush, and C. Scovel, Density Level Detection is Classification. In Neural Information Processing Systems 17, pp. 1337-1344, (2005). Los Alamos National Laboratory Technical Report LA-UR-04-3768.   [   Abstract   |   PDF (104 KB)   ]  

I. Steinwart, Consistency of Support Vector Machines and other Regularized Kernel Machines. IEEE Transactions on Information Theory, Vol. 51, pp. 128-142, 2005.   [   Abstract   ]  

2004

I. Steinwart, Sparseness of Support Vector Machines -- Some Asymptotically Sharp Bounds. In Neural Information Processing Systems 16, pp. 1069-1076, (2004). Los Alamos National Laboratory Technical Report LA-UR-03-3643.   [   Abstract   |   PostScript (121 KB)   |   PDF (98 KB)   ]  

A. Christmann and I. Steinwart, On robustness properties of convex risk minimization methods for pattern recognition. Journal of Machine Learning Research, Vol. 5, pp. 1007-1034, 2004. Los Alamos National Laboratory Technical Report LA-UR-03-3892.   [   Abstract   |   PostScript (1.04 MB)   |   PDF (433 KB)   ]  

I. Steinwart and C. Scovel, When do Support Vector Machines Learn Fast? 16th International Symposium on Mathematical Theory of Networks and Systems, 2004.
[   PS   ]  

I. Steinwart, Entropy of Convex Hulls---some Lorentz Norm Results. J. Approx. Theory, Vol. 5, pp. 42-52, 2004.   [   Download   ]  

2003

I. Steinwart, Sparseness of Support Vector Machines. Journal of Machine Learning Research, Vol. 4, pp. 1071-1105, 2003.   [   Abstract   |   PDF (306 KB)   ]  

I. Steinwart, On the Optimal Parameter Choice in $\nu$-Support Vector Machines. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, pp. 1274-1284, 2003.

K. Mittmann and I. Steinwart, On the Existence of Continuous Modifications of Vector Valued Random Fields. Journal of the Georgian Mathematical Society, Vol. 10, pp. 311-317, 2003.

I. Steinwart, Entropy Numbers of Convex Hulls and an Application to Learning Algorithms. Arch. Math., Vol. 80, pp. 310-318, 2003.   [   Download   ]  

2002

I. Steinwart, Support Vector Machines are Universally Consistent. J. Complexity, Vol. 18, pp. 768-791, 2002.   [   Download   ]  

J. Creutzig and I. Steinwart, Metric Entropy of Convex Hulls in Type p Spaces---the Critical Case. Proc. Amer. Math. Soc., Vol. 130, pp. 733-743, 2002.   [   Download   ]  

I. Steinwart, Which Data-Dependent Bounds are Suitable for SVM's?. Technical Report, 2002.   [   PS   ]  

2001

I. Steinwart, On the Influence of the Kernel on the Consistency of Support Vector Machines. Journal of Machine Learning Research, Vol. 2, pp. 67-93, 2001.   [   PDF   ]  

2000

I. Steinwart, Entropy of C(K)-Valued Operators and some Applications. Dissertation, Friedrich-Schiller-University Jena, 2000.   [   PS   ]  

I. Steinwart, Entropy of C(K)-Valued Operators. J. Approx. Theory, Vol. 103, pp. 302-328, 2000.   [   Download   ]  

1997

I. Steinwart, Gewichtete Normungleichungen fuer Operatoren zwischen Raeumen Bochner-integrierbarer Funktionen. Diploma Thesis, Carl-von-Ossietzky University Oldenburg, 1997.   [   PS   ]