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| 강건 능동 학습 (Robust Active Learning)× | 로버스트 서포트 벡터 머신× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2006 | 2006–2009 |
| 창시자≠ | Balcan, M.-F.; Beygelzimer, A.; Langford, J. | Xu, H., Caramanis, C., & Mannor, S. |
| 유형≠ | Active learning with robustness guarantees | Robust supervised classifier / regressor |
| 원전≠ | Balcan, M.-F., Beygelzimer, A., & Langford, J. (2006). Agnostic active learning. In Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), pp. 65–72. ACM. DOI ↗ | Xu, H., Caramanis, C., & Mannor, S. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510. link ↗ |
| 별칭 | RAL, noise-tolerant active learning, robust query learning, adversarially robust active learning | Robust SVM, RSVM, noise-tolerant SVM, outlier-robust SVM |
| 관련≠ | 6 | 5 |
| 요약≠ | Robust Active Learning extends the standard active learning framework to handle noisy labels, adversarial perturbations, and unreliable oracles. Rather than assuming perfect labeling, it incorporates statistical or adversarial robustness guarantees into the query selection process, maintaining sample efficiency while tolerating corruption in the annotation process. | Robust SVM extends the standard support vector machine to resist the influence of outliers and mislabeled points. By replacing the hinge loss with a bounded or non-convex loss function — or by incorporating robust optimization constraints — it learns a decision boundary that is far less distorted by corrupted training examples, making it suitable for noisy real-world datasets where standard SVM would degrade significantly. |
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