Machine learningMachine learning
鲁棒在线学习
鲁棒在线学习将在线学习框架——模型在每次观测后进行顺序更新——进行了扩展，通过引入鲁棒性机制来防御损坏的标签、对抗性样本、重尾噪声和概念漂移。其结果是一个顺序学习器，即使在数据流包含异常值或故意扰动时，也能保持有界遗憾。
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来源
- Hazan, E. (2016). Introduction to Online Convex Optimization. Foundations and Trends in Optimization, 2(3–4), 157–325. link ↗
- Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI: 10.1561/2200000018 ↗
如何引用本页
ScholarGate. (2026, June 3). Robust Online Learning (Adversarially and Noise-Resilient Sequential Learning). ScholarGate. https://scholargate.app/zh/machine-learning/robust-online-learning
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