## Title data

Hable, Robert ; Christmann, Andreas:

**Robustness Versus Consistency in Ill-Posed Classification and Regression Problems.**

*In:* Giusti, Antonio ; Ritter, Gunter ; Vichi, Maurizio
(ed.):
Classification and Data Mining. -
Berlin
: Springer
,
2013
. - pp. 27-35

ISBN 978-3-642-28894-4

DOI: https://doi.org/10.1007/978-3-642-28894-4_4

## Abstract in another language

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.

## Further data

Item Type: | Article in a book |
---|---|

Refereed: | Yes |

Keywords: | qualitative robustness; consistency; ill-posed; classification; pattern recognition; regression; universal consistency; kernel methods |

Institutions of the University: | Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics VII - Stochastics Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics VII - Stochastics > Chair Mathematics VII - Stochastics - Univ.-Prof. Dr. Andreas Christmann Faculties Faculties > Faculty of Mathematics, Physics und Computer Science Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics |

Result of work at the UBT: | Yes |

DDC Subjects: | 300 Social sciences > 310 Statistics 500 Science > 510 Mathematics |

Date Deposited: | 19 Oct 2015 06:24 |

Last Modified: | 19 Oct 2015 07:00 |

URI: | https://eref.uni-bayreuth.de/id/eprint/20516 |