On the Robustness of Kernel-Based Pairwise Learning

Patrick Gensler () and Andreas Christmann ()
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Patrick Gensler: University of Bayreuth, Department of Mathematics
Andreas Christmann: University of Bayreuth, Department of Mathematics

A chapter in Artificial Intelligence, Big Data and Data Science in Statistics, 2022, pp 111-153 from Springer

Abstract: Abstract 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 [11] 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.

Keywords: Kernel methods; Machine learning; Support vector machines; Robust statistics (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-07155-3_5

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DOI: 10.1007/978-3-031-07155-3_5

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