Sammenlign metoder

	Ensemble Online Læring	Aktiv læring
Fagfelt	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Opprinnelsesår	2001	2009
Opphavsperson	Oza, N. C. & Russell, S.	Burr Settles
Type	Ensemble (online / incremental)	Interactive supervised learning framework
Opprinnelig kilde	Oza, N. C., & Russell, S. (2001). Online bagging and boosting. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001), pp. 229–236.	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.
Alias	online ensemble methods, streaming ensemble learning, incremental ensemble learning, adaptive ensemble learning	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme
Relaterte	6	2
Sammendrag	Ensemble Online Learning combines multiple base learners that are trained incrementally on a stream of data, updating each model one observation at a time. By aggregating the predictions of diverse online learners, the ensemble achieves accuracy and robustness that surpass any single incremental model, while adapting continuously to changing data distributions.	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.