So sánh phương pháp

	Học trực tuyến tập thể	Voting Ensemble
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2001	1990s–2004
Người khởi xướng	Oza, N. C. & Russell, S.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Loại	Ensemble (online / incremental)	Ensemble (combination of multiple classifiers by vote)
Công trình gốc	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
Tên gọi khác	online ensemble methods, streaming ensemble learning, incremental ensemble learning, adaptive ensemble learning	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Liên quan	6	5
Tóm tắt	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.