方法对比
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| 鲁棒在线学习× | 鲁棒梯度提升× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2000s–2010s | 2001 |
| 提出者≠ | Hazan, E.; Shalev-Shwartz, S.; and others | Friedman, J. H. (with Huber loss from Huber, P. J.) |
| 类型≠ | Algorithmic framework | Ensemble (boosted trees with robust loss) |
| 开创性文献≠ | Hazan, E. (2016). Introduction to Online Convex Optimization. Foundations and Trends in Optimization, 2(3–4), 157–325. link ↗ | Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| 别名 | ROL, robust incremental learning, adversarially robust online learning, robust sequential learning | gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees |
| 相关≠ | 5 | 6 |
| 摘要≠ | Robust Online Learning extends the online learning framework — where a model updates sequentially after each observation — by incorporating robustness mechanisms that guard against corrupted labels, adversarial examples, heavy-tailed noise, and concept drift. The result is a sequential learner that maintains bounded regret even when the data stream contains outliers or deliberate perturbations. | Robust Gradient Boosting is gradient boosting trained with outlier-resistant loss functions — most commonly the Huber loss or quantile (pinball) loss — instead of squared-error loss. Proposed in Friedman's seminal 2001 paper, this variant produces predictions far less distorted by extreme values or contaminated labels, while retaining the full predictive power of gradient-boosted trees. |
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