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On the robustness of kernel-based pairwise learning

Title data

Gensler, Patrick ; Christmann, Andreas:
On the robustness of kernel-based pairwise learning.
Bayreuth , 2020 . - 34 S.

Official URL: Volltext

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 (2016) by allowing the prediction function to take two arguments and can thus be applied in a variety of situations such as ranking.

Further data

Item Type: Preprint, postprint
Keywords: robustness; influence function; consistency; machine learning; pairwise loss function; regularized risk
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics VII - Stochastics > Chair Mathematics VII - Stochastics - Univ.-Prof. Dr. Andreas Christmann
Result of work at the UBT: Yes
DDC Subjects: 500 Science > 510 Mathematics
Date Deposited: 04 Nov 2020 10:28
Last Modified: 04 Nov 2020 10:28
URI: https://eref.uni-bayreuth.de/id/eprint/59023