## Title data

Köhler, Hannes:

**Lp- and Risk Consistency of Localized SVMs.**

Bayreuth
,
2023
. - 29 p.

DOI: https://doi.org/10.48550/arXiv.2305.09385

## Abstract in another language

Kernel-based regularized risk minimizers, also called support vector machines (SVMs), are known to possess many desirable properties but suffer from their super-linear computational requirements when dealing with large data sets. This problem can be tackled by using localized SVMs instead, which also offer the additional advantage of being able to apply different hyperparameters to different regions of the input space. In this paper, localized SVMs are analyzed with regards to their consistency. It is proven that they inherit $L_p$- as well as risk consistency from global SVMs under very weak conditions and even if the regions underlying the localized SVMs are allowed to change as the size of the training data set increases.

## Further data

Item Type: | Preprint, postprint |
---|---|

Keywords: | localized learning; consistency; kernel methods; support vector machines; big data |

Institutions of the University: | 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 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 |

Result of work at the UBT: | Yes |

DDC Subjects: | 500 Science 500 Science > 510 Mathematics |

Date Deposited: | 19 May 2023 07:31 |

Last Modified: | 19 May 2023 07:31 |

URI: | https://eref.uni-bayreuth.de/id/eprint/76463 |