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2020 – today
- 2023
[i6]Zheng-Chu Guo, Andreas Christmann, Lei Shi:
Optimality of Robust Online Learning. CoRR abs/2304.10060 (2023)- 2022
- [j15]Hannes Köhler, Andreas Christmann:
Total Stability of SVMs and Localized SVMs. J. Mach. Learn. Res. 23: 100:1-100:41 (2022) - 2021
- [i5]Hannes Köhler, Andreas Christmann
:
Total Stability of SVMs and Localized SVMs. CoRR abs/2101.12678 (2021) - 2020
- [i4]Patrick Gensler, Andreas Christmann:
On the robustness of kernel-based pairwise learning. CoRR abs/2010.15527 (2020)
2010 – 2019
- 2018
- [j14]Andreas Christmann, Dao-Hong Xiang, Ding-Xuan Zhou:
Total stability of kernel methods. Neurocomputing 289: 101-118 (2018) - [j13]Florian Dumpert, Andreas Christmann:
Universal consistency and robustness of localized support vector machines. Neurocomputing 315: 96-106 (2018) - 2017
- [i3]Andreas Christmann, Dao-Hong Xiang, Ding-Xuan Zhou:
Total stability of kernel methods. CoRR abs/1709.07625 (2017) - 2016
- [j12]Andreas Christmann, Ding-Xuan Zhou:
On the robustness of regularized pairwise learning methods based on kernels. J. Complex. 37: 1-33 (2016) - [i2]Andreas Christmann, Florian Dumpert, Dao-Hong Xiang:
A short note on extension theorems and their connection to universal consistency in machine learning. CoRR abs/1604.04505 (2016) - 2013
- [c9]Andreas Christmann, Robert Hable:
On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel-Based Methods. Empirical Inference 2013: 231-244 - [p1]Robert Hable, Andreas Christmann:
Robustness Versus Consistency in Ill-Posed Classification and Regression Problems. Classification and Data Mining 2013: 27-35 - [i1]Andreas Christmann, Robert Hable:
On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel Based Methods. CoRR abs/1301.6944 (2013) - 2012
- [j11]Andreas Christmann, Robert Hable:
Consistency of support vector machines using additive kernels for additive models. Comput. Stat. Data Anal. 56(4): 854-873 (2012) - 2011
- [j10]Robert Hable, Andreas Christmann:
On qualitative robustness of support vector machines. J. Multivar. Anal. 102(6): 993-1007 (2011) - 2010
- [j9]Arnout Van Messem, Andreas Christmann:
A review on consistency and robustness properties of support vector machines for heavy-tailed distributions. Adv. Data Anal. Classif. 4(2-3): 199-220 (2010) - [j8]Michiel Debruyne, Andreas Christmann, Mia Hubert, Johan A. K. Suykens:
Robustness of reweighted Least Squares Kernel Based Regression. J. Multivar. Anal. 101(2): 447-463 (2010) - [c8]Andreas Christmann, Ingo Steinwart:
Universal Kernels on Non-Standard Input Spaces. NIPS 2010: 406-414
2000 – 2009
- 2009
- [c7]Ingo Steinwart, Andreas Christmann:
Fast Learning from Non-i.i.d. Observations. NIPS 2009: 1768-1776 - 2008
- [b1]Ingo Steinwart, Andreas Christmann:
Support Vector Machines. Information science and statistics, Springer 2008, ISBN 978-0-387-77241-7, pp. I-XVI, 1-601 - [j7]Andreas Christmann, Arnout Van Messem:
Bouligand Derivatives and Robustness of Support Vector Machines for Regression. J. Mach. Learn. Res. 9: 915-936 (2008) - [c6]Ingo Steinwart, Andreas Christmann:
Sparsity of SVMs that use the epsilon-insensitive loss. NIPS 2008: 1569-1576 - 2007
- [j6]B. Vandewalle, Jan Beirlant, Andreas Christmann, Mia Hubert:
A robust estimator for the tail index of Pareto-type distributions. Comput. Stat. Data Anal. 51(12): 6252-6268 (2007) - [j5]Andreas Christmann, Ingo Steinwart, Mia Hubert:
Robust learning from bites for data mining. Comput. Stat. Data Anal. 52(1): 347-361 (2007) - [c5]Andreas Christmann, Ingo Steinwart:
How SVMs can estimate quantiles and the median. NIPS 2007: 305-312 - 2004
- [j4]Andreas Christmann, Ingo Steinwart:
On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition. J. Mach. Learn. Res. 5: 1007-1034 (2004) - [c4]Andreas Christmann:
On a Combination of Convex Risk Minimization Methods. GfKl 2004: 434-441 - 2003
- [j3]Peter J. Rousseeuw, Andreas Christmann:
Robustness against separation and outliers in logistic regression. Comput. Stat. Data Anal. 43(3): 315-332 (2003) - [c3]Andreas Christmann:
Regression depth and support vector machine. Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications 2003: 71-85 - 2002
- [j2]Andreas Christmann, Paul Fischer, Thorsten Joachims:
Comparison between various regression depth methods and the support vector machine to approximate the minimum number of misclassifications. Comput. Stat. 17(2): 273-287 (2002) - [c2]Andreas Christmann, Paul Fischer, Thorsten Joachims:
Classification Based on the Support Vector Machine, Regression Depth, and Discriminant Analysis. COMPSTAT 2002: 225-230
1990 – 1999
- 1999
- [j1]Andreas Christmann:
On group sequential tests based on robust location and scale estimators in the two-sample problem. Comput. Stat. 14(3): 339-353 (1999) - 1997
- [c1]Andreas Christmann, Günter Schwichtenberg:
KIT200x - Eine Expertenumfrage über die Zukunft der Kommunikations- und Informationstechnik jenseits der Jahrtausendwende. GI-Fachtagung über Rechenzentren 1997: 1-26
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