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| 강건 능동 학습 (Robust Active Learning)× | 로버스트 랜덤 포레스트× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2006 | 2000s–2010s |
| 창시자≠ | Balcan, M.-F.; Beygelzimer, A.; Langford, J. | Various (extensions of Breiman 2001 Random Forest) |
| 유형≠ | Active learning with robustness guarantees | Robust Ensemble (noise-tolerant bagging of decision trees) |
| 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 ↗ | Chen, S., & Guestrin, C. (2019). Robust Random Forest. In Proceedings of the 36th International Conference on Machine Learning (ICML). Also see: Gao, W., & Zhou, Z.-H. (2013). On the Doubt about Margin Explanation of Boosting. Artificial Intelligence, 203, 1–18. link ↗ |
| 별칭 | RAL, noise-tolerant active learning, robust query learning, adversarially robust active learning | RRF, noise-robust random forest, outlier-resistant random forest, robust ensemble forest |
| 관련 | 6 | 6 |
| 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 Random Forest extends the standard Random Forest ensemble by incorporating mechanisms that reduce the influence of outliers, label noise, and corrupted observations. Rather than treating all training instances equally, it applies weighting or filtering strategies so that noisy or anomalous samples contribute less to individual tree splits, yielding predictions that remain reliable even when data quality is imperfect. |
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