Comparer des méthodes

	Régression logistique avec apprentissage actif	Apprentissage semi-supervisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1994–2010	1970s–2006 (formalized)
Auteur d'origine	Lewis, D. D. & Gale, W. A.; Settles, B. (survey)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Type	Active learning framework with logistic regression base learner	Learning paradigm
Source fondatrice	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
Alias	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
Apparentées	4	5
Résumé	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.