方法对比
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| 主动学习逻辑回归× | 逻辑回归× | |
|---|---|---|
| 领域≠ | 机器学习 | 研究统计学 |
| 方法族≠ | Machine learning | Process / pipeline |
| 起源年份≠ | 1994–2010 | 1958 |
| 提出者≠ | Lewis, D. D. & Gale, W. A.; Settles, B. (survey) | David Roxbee Cox |
| 类型≠ | Active learning framework with logistic regression base learner | Method |
| 开创性文献≠ | Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison. link ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| 别名≠ | AL-LR, logistic regression active learner, uncertainty sampling logistic regression, pool-based active logistic classifier | logit model, binomial logistic regression, LR |
| 相关≠ | 4 | 3 |
| 摘要≠ | Active Learning with Logistic Regression is an iterative label-efficient framework in which a logistic regression model selects the unlabeled examples it is most uncertain about, an oracle (human annotator) labels them, and the model is retrained — repeating until a labeling budget or accuracy target is met. It dramatically reduces annotation cost compared to random labeling. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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