Linganisha mbinu

	Kujifunza kwa pamoja mtandaoni	Kikundi cha Kura (Voting Ensemble)
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2001	1990s–2004
Mwanzilishi	Oza, N. C. & Russell, S.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Aina	Ensemble (online / incremental)	Ensemble (combination of multiple classifiers by vote)
Chanzo asilia	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
Majina mbadala	online ensemble methods, streaming ensemble learning, incremental ensemble learning, adaptive ensemble learning	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Zinazohusiana	6	5
Muhtasari	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.