Research map / Learning theory and algorithms / Combinatorial and search optimization
Learning SMaLL Predictors
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use two-prototype linear prediction instead of linear classifiers | chscase funds toy dataset · chscase funds | test accuracy: 0.73 | Helped |
| Use SMaLL with k=n instead of the compared standard classifiers | low-dimensional OpenML datasets · low-dimensional OpenML datasets | test accuracy | Mixed |
| Use SMaLL with k=3 and p=2 | Breast Cancer dataset · Breast Cancer | test accuracy: over 94% | Helped |
| Use sparse SMaLL prototypes instead of the described L1/elastic-net baselines | high-dimensional OpenML datasets · high-dimensional OpenML datasets | test accuracy | Helped |
| Use SMaLL instead of the most accurate compared method | high-dimensional OpenML datasets · fri_c0_1000_50 | accuracy per selected feature: over 14 | Helped |
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