Linganisha mbinu

	Ensemble Upigaji Kura Mtandaoni	Kupatanisha Kwa Kupiga Kura Kulisaidika kwa Nusu
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2001–2009	1998–2005
Mwanzilishi	Oza, N. C. & Russell, S.; extended by Bifet et al.	Zhou, Z.-H. & Li, M. (tri-training); Blum & Mitchell (co-training)
Aina	Online ensemble (incremental majority vote)	Semi-supervised ensemble (voting)
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.	Zhou, Z.-H., & Li, M. (2005). Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 17(11), 1529–1541.
Majina mbadala	streaming voting ensemble, incremental voting ensemble, online majority-vote ensemble, data-stream voting classifier	semi-supervised majority vote, SSL voting ensemble, co-training voting classifier, semi-supervised multi-classifier voting
Zinazohusiana	6	5
Muhtasari	Online Voting Ensemble is an incremental ensemble method that maintains a pool of base classifiers — each updated continuously on arriving data — and combines their predictions through a weighted or unweighted majority vote. Designed for data streams, it adapts to non-stationary distributions without retraining from scratch, making it well-suited to real-time classification tasks where data arrives sequentially and concept drift may occur.	A semi-supervised voting ensemble trains multiple classifiers on a small labeled set, then iteratively exploits unlabeled data by having the classifiers label examples they agree on, expanding the training pool until all classifiers vote jointly on test examples. It combines the label-efficiency of semi-supervised learning with the variance-reduction of majority-vote ensembles, making it valuable when annotation is costly.