方法对比

	Ensemble Online Learning	投票集成 (Voting Ensemble)
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	2001	1990s–2004
提出者	Oza, N. C. & Russell, S.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
类型	Ensemble (online / incremental)	Ensemble (combination of multiple classifiers by vote)
开创性文献	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
别名	online ensemble methods, streaming ensemble learning, incremental ensemble learning, adaptive ensemble learning	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
相关	6	5
摘要	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.