Machine learningMachine learning
在线主动学习
在线主动学习结合了两种互补的范式：它将数据作为流进行处理（在线学习），并且仅为信息量最大的实例选择性地请求标签（主动学习）。其结果是模型能够持续适应新数据，同时保持较低的标注成本——这在标注数据昂贵且样本按顺序到达而非一次性全部到达的情况下非常有用。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Cesa-Bianchi, N., Gentile, C., & Zaniboni, L. (2006). Worst-case analysis of selective sampling for linear classification. Journal of Machine Learning Research, 7, 1205–1230. link ↗
- Sculley, D. (2007). Online active learning methods for fast label-efficient spam filtering. Proceedings of the Fourth Conference on Email and Anti-Spam (CEAS 2007). link ↗
如何引用本页
ScholarGate. (2026, June 3). Online Active Learning (Streaming Active Learning). ScholarGate. https://scholargate.app/zh/machine-learning/online-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.
- 主动学习机器学习↔ compare
- 少样本学习机器学习↔ compare
- 在线学习机器学习↔ compare
- 在线逻辑回归机器学习↔ compare
- 在线随机森林机器学习↔ compare
- 半监督学习机器学习↔ compare