A. Christmann

发表

Support vector machines

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2008, Data Mining and Knowledge Discovery Handbook.

Estimating conditional quantiles with the help of the pinball loss

Ingo Steinwart, A. Christmann, 2011, 1102.2101.

Consistency and robustness of kernel-based regression in convex risk minimization

Ingo Steinwart, A. Christmann, 2007, 0709.0626.

Consistency and robustness of kernel based regression

Ingo Steinwart, A. Christmann, 2005 .

On consistency and robustness properties of support vector machines for heavy-tailed distributions

Arnout Van Messem, Ingo Steinwart, A. Christmann, 2010 .

TR-06-09 ROBUSTNESS AND STABILITY OF REWEIGHTED KERNEL BASED REGRESSION

M. Debruyne, M. Hubert, J. Suykens, 2006 .

Universal Kernels on Non-Standard Input Spaces

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2010, NIPS.

Consistency of kernel-based quantile regression

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2008 .

Learning rates for the risk of kernel-based quantile regression estimators in additive models

Ding-Xuan Zhou, A. Christmann, 2014, 1405.3379.

Sparsity of SVMs that use the ∊-insensitive loss

Ingo Steinwart, A. Christmann, 2008, NIPS 2008.

Estimation of scale functions to model heteroscedasticity by regularised kernel-based quantile methods

A. Christmann, R. Hable, 2014 .

Qualitative Robustness of Bootstrap Approximations for Kernel Based Methods

A. Christmann, M. Salibian-Barrera, S. V. Aelst, 2013 .

Estimation of scale functions to model heteroscedasticity by support vector machines

A. Christmann, R. Hable, 2011, 1111.1830.

Classification Based on the Support Vector Machine and on Regression Depth

A. Christmann, 2002 .

Support Vector Machines for Additive Models: Consistency and Robustness

A. Christmann, R. Hable, 2010, 1007.4062.

Regression depth and support vector machine

A. Christmann, 2003, Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications.

Insurance: an R-Program to Model Insurance Data

A. Christmann, M. Marin-Galiano, 2004 .

On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2004, J. Mach. Learn. Res..

On the bootstrap approach for support vector machines and related kernel based methods

Robert Hable, Andreas Christmann, A. Christmann, 2013 .

Determination of hyper-parameters for kernel based classification and regression

Andreas Christmann, Stefan Rüping, Karsten Luebke, 2005 .

Least median of weighted squares in logistic regression with large strata

Andreas Christmann, A. Christmann, 1994 .

Measuring overlap in logistic regression

Andreas Christmann, Peter J. Rousseeuw, P. Rousseeuw, 1999 .

Measuring overlap in binary regression

Andreas Christmann, Peter J. Rousseeuw, P. Rousseeuw, 2001 .

On the stability of bootstrap estimators

A. Christmann, M. Salibian-Barrera, S. V. Aelst, 2011, 1111.1876.

On the Stability of Bootstrap

A. Christmann, S. V. Aelst, M. Salib, 2011 .

On Consistency and Robustness of Support Vector Machines

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2009 .

An approach to model complex high–dimensional insurance data

Andreas Christmann, A. Christmann, 2004 .

Comparison between the regression depth method and the support vector machine to approximate the minimum number of misclassifications

T. Joachims, A. Christmann, P. Fischer, 2000 .

On non-parametric robust quantile regression by support vector machines

Andreas Christmann, A. Christmann, 2008 .

Qualitatively Robust Nonparametric Regression By Support Vector Machines

Robert Hable, Andreas Christmann, A. Christmann, 2010 .

On Support Vector Machines to Estimate Scale Functions

Robert Hable, Andreas Christmann, A. Christmann, 2011 .

Support vector machine

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2013 .

Regression depth and support vector machine

Andreas Christmann, A. Christmann, 2003, Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications.

Robustness of reweighted Least Squares Kernel Based Regression

Johan A. K. Suykens, Andreas Christmann, Mia Hubert, 2010, J. Multivar. Anal..

A review on consistency and robustness properties of support vector machines for heavy-tailed distributions

Andreas Christmann, Arnout Van Messem, A. Christmann, 2010, Adv. Data Anal. Classif..

Robust Learning from Bites

Andreas Christmann, A. Christmann, 2005 .

On Properties of Support Vector Machines for Pattern Recognition in Finite Samples

Andreas Christmann, A. Christmann, 2004 .

On a Combination of Convex Risk Minimization Methods

Andreas Christmann, A. Christmann, 2004, GfKl.

Comparison between various regression depth methods and the support vector machine to approximate the minimum number of missclassifications

Thorsten Joachims, Andreas Christmann, Paul Fischer, 2002, Comput. Stat..

Classification Based on the Support Vector Machine, Regression Depth, and Discriminant Analysis

Thorsten Joachims, Andreas Christmann, Paul Fischer, 2002, COMPSTAT.

Robust learning from bites for data mining

Andreas Christmann, Mia Hubert, Ingo Steinwart, 2007, Comput. Stat. Data Anal..

A short note on extension theorems and their connection to universal consistency in machine learning pdf

Andreas Christmann, Dao-Hong Xiang, Florian Dumpert, 2016, ArXiv.

On the robustness of regularized pairwise learning methods based on kernels

Andreas Christmann, Ding-Xuan Zhou, Ding-Xuan Zhou, 2015, J. Complex..

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

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2009, NIPS.

On group sequential tests based on robust location and scale estimators in the two-sample problem

Andreas Christmann, A. Christmann, 1999, Comput. Stat..

Some properties of the length of the shortest half

Andreas Christmann, Ursula Gather, U. Gather, 1994 .

On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel Based Methods

Robert Hable, Andreas Christmann, A. Christmann, 2013, Empirical Inference.

Editorial: On the interface of statistics and machine learning

Andreas Christmann, Xiaotong Shen, Xiaotong Shen, 2009 .

Robust estimation of Cronbach's alpha

Stefan Van Aelst, Andreas Christmann, A. Christmann, 2006 .

How SVMs can estimate quantiles and the median

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2007, NIPS.

Optimality of Robust Online Learning

A. Christmann, Zheng-Chu Guo, Lei Shi, 2023, Foundations of Computational Mathematics.

Robustness Versus Consistency in Ill-Posed Classification and Regression Problems

Robert Hable, Andreas Christmann, A. Christmann, 2013, Classification and Data Mining.

On qualitative robustness of support vector machines

Robert Hable, Andreas Christmann, A. Christmann, 2009, J. Multivar. Anal..

On the robustness of kernel-based pairwise learning pdf

Andreas Christmann, Patrick Gensler, A. Christmann, 2020, ArXiv.

A robust estimator for the tail index of Pareto-type distributions

Andreas Christmann, Mia Hubert, B. Vandewalle, 2007, Comput. Stat. Data Anal..

Total Stability of SVMs and Localized SVMs pdf

Andreas Christmann, Hannes Köhler, A. Christmann, 2021, J. Mach. Learn. Res..

Universal consistency and robustness of localized support vector machines

Andreas Christmann, Florian Dumpert, Florian Dumpert, 2017, Neurocomputing.

Robust Estimation in Generalized Linear Models

A. Christmann, 2001 .

Consistency of support vector machines using additive kernels for additive models

Robert Hable, Andreas Christmann, A. Christmann, 2012, Comput. Stat. Data Anal..

Total stability of kernel methods

Andreas Christmann, Dao-Hong Xiang, Ding-Xuan Zhou, 2017, Neurocomputing.

Bouligand Derivatives and Robustness of Support Vector Machines for Regression

Andreas Christmann, Arnout Van Messem, A. Christmann, 2007, J. Mach. Learn. Res..

The hidden logistic regression model

P. Rousseeuw, A. Christmann, 2001 .

Positive breakdown point estimation in categorical regression models

A. Christmann, 1998 .

Robustness against separation and outliers in logistic regression

Andreas Christmann, Peter Rousseeuw, P. Rousseeuw, 2003, Comput. Stat. Data Anal..