Comparar métodos

	Aprendizagem Online em Conjunto	Comitê de Votação
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2001	1990s–2004
Autor original	Oza, N. C. & Russell, S.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Tipo	Ensemble (online / incremental)	Ensemble (combination of multiple classifiers by vote)
Fonte seminal	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
Outros nomes	online ensemble methods, streaming ensemble learning, incremental ensemble learning, adaptive ensemble learning	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Relacionados	6	5
Resumo	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.