Módszerek összehasonlítása

	Logisztikus regresszióval végzett aktív tanulás	Félfelügyelt tanulás
Tudományterület	Gépi tanulás	Gépi tanulás
Módszercsalád	Machine learning	Machine learning
Keletkezés éve	1994–2010	1970s–2006 (formalized)
Megalkotó	Lewis, D. D. & Gale, W. A.; Settles, B. (survey)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Típus	Active learning framework with logistic regression base learner	Learning paradigm
Alapmű	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
Alternatív nevek	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
Kapcsolódó	4	5
Összefoglaló	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.