## Title data

Christmann, Andreas ; Zhou, Ding-Xuan:

**On the Robustness of Regularized Pairwise Learning Methods Based on Kernels.**

*In:* Journal of Complexity.
Vol. 37
(2016)
.
- pp. 1-33.

ISSN 0885-064X

DOI: https://doi.org/10.1016/j.jco.2016.07.001

## Related URLs

## Project information

Project financing: |
Deutsche Forschungsgemeinschaft |
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## Abstract in another language

Regularized empirical risk minimization including support vector machines plays an important role in machine learning theory. In this paper regularized pairwise learning (RPL) methods based on kernels will be investigated. One example is regularized minimization of the error entropy loss which has recently attracted quite some interest from the viewpoint of consistency and learning rates. This paper shows that such RPL methods have additionally good statistical robustness properties, if the loss function and the kernel are chosen appropriately. We treat two cases of particular interest: (i) a bounded and non-convex loss function and (ii) an unbounded convex loss function satisfying a certain Lipschitz type condition.

## Further data

Item Type: | Article in a journal |
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Refereed: | Yes |

Keywords: | machine learning; pairwise loss function; regularized risk; robustness |

Institutions of the University: | Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics VII - Stochastics and Machine Learning > Chair Mathematics VII - Stochastics and mashine learning - 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 Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics VII - Stochastics and Machine Learning |

Result of work at the UBT: | Yes |

DDC Subjects: | 500 Science > 510 Mathematics |

Date Deposited: | 14 Oct 2015 09:03 |

Last Modified: | 07 Aug 2023 13:14 |

URI: | https://eref.uni-bayreuth.de/id/eprint/20475 |