Research map / Learning theory and algorithms / Kernels and regression theory
Greedy Feature Construction
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 |
|---|---|---|---|
| Replace A la carte with GFC | feature construction, 500 features · 16 regression datasets | RMSE x100: 12.70 -> 11.17 | Mixed |
| Replace A la carte with GFC | feature construction, 500 features · 16 regression datasets | walltime per fold: 11 : 58 : 41 -> 01 : 30 : 28 on Year Prediction MSD | Helped |
| GFC with 500 features vs A la carte with 100 features | feature construction · 16 regression datasets | RMSE: T = 28 vs threshold 30 | Helped |
| GFC vs A la carte at equal feature count (100 features) | feature construction · 16 regression datasets | RMSE: T = 60 | No clear effect |
| GFC vs A la carte at equal feature count (500 features) | feature construction · 16 regression datasets | RMSE: T = 42 | Helped |
| Replace random Fourier features (Gaussian, Laplace, Cauchy) with GFC | feature construction · 16 regression datasets | RMSE | Helped |
| Increase features from 100 to 500 in GFC | GFC · pole-telecom | RMSE x100: 7.34 (±0.29) -> 5.55 (±0.15) | Helped |
| Increase features from 100 to 500 in GFC | GFC · kinematics | RMSE x100: 5.18 (±0.09) -> 4.65 (±0.11) | Helped |
| Increase features from 100 to 500 in GFC | GFC · bank | RMSE x100: 9.83 (±0.27) -> 9.83 (±0.30) | No clear effect |
Closest papers by meaning
- Joint Representative Selection and Feature Learning: A Semi-Supervised Approach (CVPR 2019 · similarity 0.84)
- Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels (NeurIPS 2018 · similarity 0.84)
- Understanding Randomization in Greedy Model Search (NeurIPS 2026 · similarity 0.84)
- Generator Assisted Mixture of Experts for Feature Acquisition in Batch (AAAI 2024 · similarity 0.83)
- CGMatch: A Different Perspective of Semi-supervised Learning (CVPR 2025 · similarity 0.82)
- SAND: One-Shot Feature Selection with Additive Noise Distortion (ICML 2025 · similarity 0.82)
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.