Machine learning foundations· 2007
Random Features for Large-Scale Kernel Machines
On the reading lists
- #270 in the Top 500 (2026 edition)
- #21 in Machine learning foundations (2026 edition)
Why it matters
Introduced randomized Fourier features, enabling scalable approximations to nonlinear kernel learning.
Condensed from the expert lists' notes.
The record
- First released
- 2007 (from OpenAlex)
- Citations
- 5,335 citations, as counted by
Semantic Scholar (non-commercial licence) - Awards
- NeurIPS Test of Time
- Named by
- the chatgpt list only
- Score
- 0.469 · computed rank 270 · consensus 0.13, must-read 0, citations percentile 0.8 (2000s · foundations), awards 1, existing lists 0. Method
Data sources
Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0), and Semantic Scholar (used under its non-commercial API licence). Each count names its source. See the method.