Robustness Versus Consistency in Ill-Posed Classification and Regression Problems

Titelangaben

Hable, Robert ; Christmann, Andreas:
Robustness Versus Consistency in Ill-Posed Classification and Regression Problems.
In: Giusti, Antonio ; Ritter, Gunter ; Vichi, Maurizio (Hrsg.): Classification and Data Mining. - Berlin : Springer , 2013 . - S. 27-35
ISBN 978-3-642-28894-4
DOI: https://doi.org/10.1007/978-3-642-28894-4_4

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Abstract

It is well-known from parametric statistics that there can be a goal conflict between efficiency and robustness. However, in so-called ill-posed problems, there is even a goal conflict between consistency and robustness. This particularly applies to certain nonparametric statistical problems such as nonparametric classification and regression problems which are often ill-posed. As an example in statistical machine learning, support vector machines are considered.