2022
DOI: 10.1609/aaai.v36i8.20803
|View full text |Cite
|
Sign up to set email alerts
|

TRF: Learning Kernels with Tuned Random Features

Abstract: Random Fourier features (RFF) are a popular set of tools for constructing low-dimensional approximations of translation-invariant kernels, allowing kernel methods to be scaled to big data. Apart from their computational advantages, by working in the spectral domain random Fourier features expose the translation invariant kernel as a density function that may, in principle, be manipulated directly to tune the kernel. In this paper we propose selecting the density function from a reproducing kernel Hilbert spa… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 37 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?