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arXiv Machine Learning By Taha Bouhsine

Bernstein-Schur Kernels: Random Features by Sketched Modulation and Radial Randomization

Read the original on arXiv Machine Learning →

arXiv:2606. 11255v1 Announce Type: new Abstract: Bernstein--Schur kernels are products of a finite-feature kernel (one with an explicit finite-dimensional feature map) and a completely monotone shift-invariant kernel: nonstationary kernels that fall between the shift-invariant and dot-product templates random features usually exploit, so in general neither Bochner sampling nor polynomial sketching applies to the full kernel directly.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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