Machine learningMachine learning
鲁棒主动学习
鲁棒主动学习将标准主动学习框架扩展到处理带噪声标签、对抗性扰动以及不可靠或不准确的预言家。它不假设标签完美无误，而是将统计或对抗鲁棒性保证纳入查询选择过程，在容忍标注过程中的错误的同时保持样本效率。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- 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: 10.1145/1143844.1143853 ↗
- Settles, B. (2009). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison. link ↗
如何引用本页
ScholarGate. (2026, June 3). Robust Active Learning (Noise-Tolerant Query-Based Learning). ScholarGate. https://scholargate.app/zh/machine-learning/robust-active-learning
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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