Research map / Learning theory and algorithms / Kernels and regression theory
Sparse Reject Option Classifier Using Successive Linear Programming
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SDR-SVM instead of ER-SVM | Reject option classification · Ionosphere, Parkinsons, Heart, ILPD, Pima Indian Diabetes | cross-validation risk | Helped |
| Use SDR-SVM instead of Double hinge SVM | Reject option classification · Parkinsons, Heart | cross-validation risk | Helped |
| Use SDR-SVM instead of Double hinge SVM | Reject option classification · ILPD, Ionosphere, Pima Indian Diabetes | cross-validation risk | No clear effect |
| Use SDR-SVM instead of Normal-SVM | Reject option classification · Parkinsons, Heart, ILPD, Pima Indian Diabetes | cross-validation risk | Helped |
| Use SDR-SVM instead of Normal-SVM | Reject option classification · Ionosphere | cross-validation risk | No clear effect |
| Use SDR-SVM instead of other compared approaches | Reject option classification · Ionosphere, Heart, Parkinsons | rejection rate | Helped |
| Use SDR-SVM instead of Double hinge SVM | Reject option classification · Pima Indian Diabetes, ILPD | rejection rate | No clear effect |
| Use SDR-SVM instead of ER-SVM | Reject option classification · All five datasets | accuracy on unrejected examples | Helped |
| Use SDR-SVM instead of Normal-SVM | Reject option classification · ILPD, Parkinsons | accuracy on unrejected examples | Helped |
| Use SDR-SVM instead of Normal-SVM | Reject option classification · Ionosphere, Heart, Pima Indian Diabetes | accuracy on unrejected examples | No clear effect |
| Use SDR-SVM instead of Double hinge SVM | Reject option classification · All five datasets | accuracy on unrejected examples | No clear effect |
| Use SDR-SVM instead of Double hinge SVM and Normal-SVM | Reject option classification · ILPD, Ionosphere, Pima Indian Diabetes | number of support vectors | Helped |
Closest papers by meaning
- Online Active Learning of Reject Option Classifiers (AAAI 2020 · similarity 0.86)
- On discriminative learning of prediction uncertainty (ICML 2019 · similarity 0.85)
- Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses (NeurIPS 2022 · similarity 0.84)
- Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice (ICML 2023 · similarity 0.84)
- Rejection via Learning Density Ratios (NeurIPS 2024 · similarity 0.83)
- On the Convergence of Loss and Uncertainty-based Active Learning Algorithms (NeurIPS 2024 · similarity 0.83)
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