Research map / Learning theory and algorithms / Prediction and decision losses
Multiclass Capped ℓp-Norm SVM for Robust Classifications
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace hinge loss with capped lp-norm hinge loss (CappedSVM vs SVM) | multiclass linear SVM · ALLAML | accuracy (%): 97.13 -> 97.14 | No clear effect |
| replace hinge loss with capped lp-norm hinge loss (CappedSVM vs SVM) | multiclass linear SVM · LUNG | accuracy (%): 96.47 -> 98.03 | Helped |
| replace hinge loss with capped lp-norm hinge loss (CappedSVM vs SVM) | multiclass linear SVM · Carcinomas | accuracy (%): 90.96 -> 96.66 | Helped |
| replace hinge loss with capped lp-norm hinge loss (CappedSVM vs SVM) | multiclass linear SVM · Prostate-GE | accuracy (%): 91.27 -> 93.18 | Helped |
| replace hinge loss with capped lp-norm hinge loss (CappedSVM vs SVM) | multiclass linear SVM · Wine | accuracy (%): 98.33 -> 99.44 | Helped |
| replace hinge loss with capped lp-norm hinge loss (CappedSVM vs SVM) | multiclass linear SVM · JAFFE | accuracy (%): 99.59 -> 100.00 | No clear effect |
| use CappedSVM instead of SVM/LSSVM when tuning the regularization parameter | CappedSVM · six benchmark datasets | accuracy vs parameter | Helped |
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