Comparer des méthodes

	Apprentissage Actif Semi-Supervisé	Apprentissage semi-supervisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2002	1970s–2006 (formalized)
Auteur d'origine	Muslea, I., Minton, S., & Knoblock, C. A.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Type	Hybrid learning framework	Learning paradigm
Source fondatrice	Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Alias	SSAL, active semi-supervised learning, query-based semi-supervised learning, semi-supervised learning with active queries	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Apparentées	3	5
Résumé	Semi-supervised Active Learning (SSAL) is a hybrid learning paradigm that combines active learning's selective query strategy with semi-supervised learning's ability to exploit unlabeled data. The model iteratively selects the most informative unlabeled instances for expert annotation while simultaneously leveraging the large pool of unannotated samples to improve its own representations, dramatically reducing labeling costs while maintaining strong predictive accuracy.	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.