Comparar métodos

	Active Learning Stacking Ensemble	Aprendizaje activo
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	1992–2012	2009
Autor original	Wolpert, D. H. (stacking); Settles, B. (active learning survey)	Burr Settles
Tipo	Hybrid (active learning + stacked ensemble)	Interactive supervised learning framework
Fuente seminal	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.
Alias	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
Relacionados	5	2
Resumen	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.