Compară metode

	Învățare activă cu ansambluri stratificate	Învățare activă
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	1992–2012	2009
Autorul original	Wolpert, D. H. (stacking); Settles, B. (active learning survey)	Burr Settles
Tip	Hybrid (active learning + stacked ensemble)	Interactive supervised learning framework
Sursa seminală	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.
Denumiri alternative	AL-stacking, query-by-committee stacking, active stacked generalization, stacking with active query	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme
Înrudite	5	2
Rezumat	Active Learning Stacking Ensemble combines an active learning query loop with stacked generalization: a pool of unlabeled data is available, and the model iteratively selects the most informative instances for human labeling, using those labels to train and refine a stacking ensemble of multiple base learners topped by a meta-learner. This approach reduces annotation cost while maximizing the predictive power of the ensemble.	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.