Machine learningMachine learning
主动学习逻辑回归
主动学习与逻辑回归是一种迭代式的、标签高效的框架，其中逻辑回归模型选择它最不确定的未标记示例，一个预言机（人工标注员）对其进行标记，然后重新训练模型——重复此过程，直到达到标注预算或准确率目标。与随机标注相比，它大大降低了标注成本。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison. link ↗
- Lewis, D. D., & Gale, W. A. (1994). A sequential algorithm for training text classifiers. Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 3–12. DOI: 10.1007/978-1-4471-2099-5_1 ↗
如何引用本页
ScholarGate. (2026, June 3). Active Learning with Logistic Regression (Uncertainty Sampling). ScholarGate. https://scholargate.app/zh/machine-learning/active-learning-logistic-regression
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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