방법 비교

	앙상블 온라인 학습	Voting Ensemble
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2001	1990s–2004
창시자	Oza, N. C. & Russell, S.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
유형	Ensemble (online / incremental)	Ensemble (combination of multiple classifiers by vote)
원전	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.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
별칭	online ensemble methods, streaming ensemble learning, incremental ensemble learning, adaptive ensemble learning	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
관련	6	5
요약	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.	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.