Research map / Learning theory and algorithms / Prediction and decision losses
Improving Efficiency of SVM k-Fold Cross-Validation by Alpha Seeding
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Reuse the previous fold's alpha values by matching removed instances to similar, same-label incoming instances | SVM k-fold cross-validation · Madelon, k=100 | elapsed time: 32 times faster | Helped |
| Reuse the previous fold's alpha values through single-instance replacement | SVM k-fold cross-validation · MNIST, k=10 | elapsed time (sec): 172,816 -> 37,822 | Helped |
| Reuse the previous fold's alpha values through single-instance replacement | SVM k-fold cross-validation · Adult, k=10 | accuracy (%): 82.36 -> 82.36 | No clear effect |
| Use gradual optimality-guided alpha adjustment rather than single-instance replacement (ablation) | SVM k-fold cross-validation · Adult, k=10 | elapsed time | Hurt |
| Replace all removed instances at once and estimate incoming-instance alphas, rather than using single-instance replacement (ablation) | SVM k-fold cross-validation · Madelon, k=10 | elapsed time | Mixed |
| Keep shared-instance alpha values unchanged and use multiple-instance replacement (ablation) | SVM k-fold cross-validation · Adult and MNIST, k=10 | training iterations | Mixed |
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