Comparer des méthodes

	Machine à vecteurs de support par apprentissage actif	Apprentissage semi-supervisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2001	1970s–2006 (formalized)
Auteur d'origine	Tong, S. & Koller, D.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Type	Active learning + kernel classifier	Learning paradigm
Source fondatrice	Tong, S., & Koller, D. (2001). Support Vector Machine Active Learning with Applications to Text Classification. Journal of Machine Learning Research, 2, 45–66.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Alias	Active SVM, AL-SVM, SVM active learning, query-by-committee SVM	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Apparentées	3	5
Résumé	Active learning SVM combines the strong decision-boundary of support vector machines with an intelligent query strategy that selects the most informative unlabeled instances for human annotation. Introduced by Tong and Koller in 2001, it achieves high classification accuracy using far fewer labeled examples than passive supervised learning, making it practical whenever labeling is expensive or slow.	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.