Research map / Learning theory and algorithms / Kernels and regression theory
TRF: Learning Kernels with Tuned Random Features
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use TRF instead of the best listed baseline | Kernel classification · Biodeg, 20% test | misclassification error (%): 12.0(2.3) -> 11.2(3.5) | Helped |
| Use TRF instead of the best listed baseline | Kernel classification · Car, 20% test | misclassification error (%): 0.75(0.1) -> 0.73(0.1) | Helped |
| Use TRF instead of the best listed baseline | Kernel classification · Contra., 20% test | misclassification error (%): 31.6(4.8) -> 29.5(3.1) | Helped |
| Use TRF instead of the best listed baseline | Kernel classification · Fertility, 20% test | misclassification error (%): 9(4.2) -> 9(4.2) | No clear effect |
| Use TRF instead of the best listed baseline | Kernel classification · Ionosph., 20% test | misclassification error (%): 3.99(1.6) -> 14.7(7.4) | Hurt |
| Use TRF instead of the best listed baseline | Kernel classification · Sonar, 20% test | misclassification error (%): 20.5(2.7) -> 15.5(3.2) | Helped |
| Use TRF instead of the best listed baseline | Kernel classification · a8a, 20% test | misclassification error (%): 12.9(2.7) -> 12.6(2.7) | Helped |
| Use TRF instead of the best listed baseline | Kernel regression · Airfoil, 20% test | RMSE: 0.46(0.02) -> 0.43(0.02) | Helped |
| Use TRF instead of the best listed baseline | Kernel regression · Auto, 20% test | RMSE: 0.17(0.04) -> 0.16(0.03) | Helped |
| Use TRF instead of the best listed baseline | Kernel regression · Boston, 20% test | RMSE: 0.38(0.07) -> 0.38(0.14) | No clear effect |
| Use TRF instead of the best listed baseline | Kernel regression · Slump, 20% test | RMSE: 0.020(0.01) -> 0.018(0.01) | Helped |
| Use TRF instead of the best listed baseline | Kernel regression · Yatch, 20% test | RMSE: 0.05(0.03) -> 0.16(0.12) | Hurt |
Closest papers by meaning
- Learning Adaptive Random Features (AAAI 2019 · similarity 0.91)
- On The Relative Error of Random Fourier Features for Preserving Kernel Distance (ICLR 2023 · similarity 0.91)
- Towards a Unified Analysis of Random Fourier Features (ICML 2019 · similarity 0.90)
- A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent (NeurIPS 2020 · similarity 0.90)
- Scalable Random Wavelet Features: Efficient Non-Stationary Kernel Approximation with Convergence Guarantees (ICLR 2026 · similarity 0.89)
- Random Fourier Features via Fast Surrogate Leverage Weighted Sampling (AAAI 2020 · similarity 0.89)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
