## Title data

Gensler, Patrick ; Christmann, Andreas:

**On the Robustness of Kernel-Based Pairwise Learning.**

*In:* Steland, Ansgar ; Tsui, Kwok-Leung
(ed.):
Artificial Intelligence, Big Data and Data Science in Statistics : Challenges and Solutions in Environmetrics, the Natural Sciences and Technology. -
Cham
: Springer
,
2022
. - pp. 111-153

ISBN 978-3-031-07154-6

DOI: https://doi.org/10.1007/978-3-031-07155-3_5

## Project information

Project financing: |
Deutsche Forschungsgemeinschaft DFG Grant CH 291/3-1 |
---|

## Abstract in another language

It is shown that many results on the statistical robustness of kernel-based pairwise learning can be derived under basically no assumptions on the input and output spaces. In particular, neither moment conditions on the conditional distribution of Y given X = x nor the boundedness of the output space is needed. We obtain results on the existence and boundedness of the influence function and show qualitative robustness of the kernel-based estimator. The present paper generalizes results by Christmann and Zhou by allowing the prediction function to take two arguments and can thus be applied in a variety of situations such as ranking, similarity learning and distance metric learning.

## Further data

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

Refereed: | Yes |

Keywords: | Kernel methods; Machine Learning; Support Vector Machines; Robust Statistics |

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: | 000 Computer Science, information, general works > 004 Computer science 500 Science > 510 Mathematics |

Date Deposited: | 20 Dec 2022 08:54 |

Last Modified: | 20 Dec 2022 08:54 |

URI: | https://eref.uni-bayreuth.de/id/eprint/73081 |