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Lp- and Risk Consistency of Localized SVMs

arXiv 2305.09385 ↗First posted 16 May 2023stat.ML cs.LGcited by 0

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Abstract (arXiv)

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 Lp- 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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 72 references that are in the map. First posted on arXiv 16 May 2023.

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18 of 72 references.

References · 18

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6 more
  1. 2015Iterative Regularization for Learning with Convex Loss Functionscited by 9
  2. 2016Spatial Decompositions for Large Scale SVMscited by 5
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  5. 2019Quantitative Robustness of Localized Support Vector Machinescited by 2
  6. 2023On the Connection between Lp and Risk Consistency and its Implications on Regularized Kernel Methodscited by 1

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