Comparer des méthodes

	Apprentissage Actif Semi-Supervisé	Apprentissage actif
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2002	2009
Auteur d'origine	Muslea, I., Minton, S., & Knoblock, C. A.	Burr Settles
Type	Hybrid learning framework	Interactive supervised learning framework
Source fondatrice	Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool.	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.
Alias	SSAL, active semi-supervised learning, query-based semi-supervised learning, semi-supervised learning with active queries	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme
Apparentées	3	2
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.	Active learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informative unlabeled examples. Formalized by Burr Settles in his seminal 2009 literature survey, active learning addresses the practical bottleneck of annotation cost by achieving high model accuracy with far fewer labeled examples than passive supervised learning requires.