So sánh phương pháp

	Hồi quy Logistic Học Chủ động	Học bán giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1994–2010	1970s–2006 (formalized)
Người khởi xướng	Lewis, D. D. & Gale, W. A.; Settles, B. (survey)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Loại	Active learning framework with logistic regression base learner	Learning paradigm
Công trình gốc	Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Tên gọi khác	AL-LR, logistic regression active learner, uncertainty sampling logistic regression, pool-based active logistic classifier	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Liên quan	4	5
Tóm tắt	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.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.