Andreas Christmann - researchr alias

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Viewing Publication 1 - 25 from 25

2022

Total Stability of SVMs and Localized SVMsHannes Köhler, Andreas Christmann. jmlr, 23, 2022. [doi]

2018

Total stability of kernel methodsAndreas Christmann, Dao-Hong Xiang, Ding-Xuan Zhou. ijon, 289:101-118, 2018. [doi]

Universal consistency and robustness of localized support vector machinesFlorian Dumpert, Andreas Christmann. ijon, 315:96-106, 2018. [doi]

2016

On the robustness of regularized pairwise learning methods based on kernelsAndreas Christmann, Ding-Xuan Zhou. jc, 37:1-33, 2016. [doi]

2013

On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel-Based MethodsAndreas Christmann, Robert Hable. BIRTHDAY 2013: 231-244 [doi]

Robustness Versus Consistency in Ill-Posed Classification and Regression ProblemsRobert Hable, Andreas Christmann. In Antonio Giusti, Gunter Ritter, Maurizio Vichi, editors, Classification and Data Mining. Studies in Classification, Data Analysis, and Knowledge Organization, pages 27-35, Springer, 2013. [doi]

2012

Consistency of support vector machines using additive kernels for additive modelsAndreas Christmann, Robert Hable. csda, 56(4):854-873, 2012. [doi]

2011

On qualitative robustness of support vector machinesRobert Hable, Andreas Christmann. ma, 102(6):993-1007, 2011. [doi]

2010

Universal Kernels on Non-Standard Input SpacesAndreas Christmann, Ingo Steinwart. nips 2010: 406-414 [doi]

Robustness of reweighted Least Squares Kernel Based RegressionMichiel Debruyne, Andreas Christmann, Mia Hubert, Johan A. K. Suykens. ma, 101(2):447-463, 2010. [doi]

A review on consistency and robustness properties of support vector machines for heavy-tailed distributionsArnout Van Messem, Andreas Christmann. adac, 4(2-3):199-220, 2010. [doi]

2009

Fast Learning from Non-i.i.d. ObservationsIngo Steinwart, Andreas Christmann. nips 2009: 1768-1776 [doi]

2008

Bouligand Derivatives and Robustness of Support Vector Machines for RegressionAndreas Christmann, Arnout Van Messem. jmlr, 9:915-936, 2008. [doi]

Support Vector MachinesIngo Steinwart, Andreas Christmann. Information science and statistics, Springer, 2008.

Sparsity of SVMs that use the epsilon-insensitive lossIngo Steinwart, Andreas Christmann. nips 2009: 1569-1576 [doi]

2007

How SVMs can estimate quantiles and the medianAndreas Christmann, Ingo Steinwart. nips 2008: 305-312 [doi]

Robust learning from bites for data miningAndreas Christmann, Ingo Steinwart, Mia Hubert. csda, 52(1):347-361, 2007. [doi]

2004

On a Combination of Convex Risk Minimization MethodsAndreas Christmann. gfkl 2005: 434-441 [doi]

On Robustness Properties of Convex Risk Minimization Methods for Pattern RecognitionAndreas Christmann, Ingo Steinwart. jmlr, 5:1007-1034, 2004. [doi]

2003

Regression depth and support vector machineAndreas Christmann. dimacs 2006: 71-86

Robustness against separation and outliers in logistic regressionPeter J. Rousseeuw, Andreas Christmann. csda, 43(3):315-332, 2003. [doi]

2002

Classification Based on the Support Vector Machine, Regression Depth, and Discriminant AnalysisAndreas Christmann, Paul Fischer, Thorsten Joachims. compstat 2002: 225-230 [doi]

Comparison between various regression depth methods and the support vector machine to approximate the minimum number of misclassificationsAndreas Christmann, Paul Fischer, Thorsten Joachims. cstat, 17(2):273-287, 2002. [doi]

1999

On group sequential tests based on robust location and scale estimators in the two-sample problemAndreas Christmann. cstat, 14(3):339-353, 1999. [doi]

1997

KIT200x - Eine Expertenumfrage über die Zukunft der Kommunikations- und Informationstechnik jenseits der JahrtausendwendeAndreas Christmann, Günter Schwichtenberg. kivs 1997: 1-26

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