Confronta i metodi

	Regressione Logistica con Apprendimento Attivo	Apprendimento semi-supervisionato
Campo	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning
Anno di origine	1994–2010	1970s–2006 (formalized)
Ideatore	Lewis, D. D. & Gale, W. A.; Settles, B. (survey)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Tipo	Active learning framework with logistic regression base learner	Learning paradigm
Fonte seminale	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
Correlati	4	5
Sintesi	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.