Linganisha mbinu

	Kujifunza kwa Njia Tendaji kwa Kuunganisha Mbinu	Kikundi cha Kura (Voting Ensemble)
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1992–2012	1990s–2004
Mwanzilishi	Wolpert, D. H. (stacking); Settles, B. (active learning survey)	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Aina	Hybrid (active learning + stacked ensemble)	Ensemble (combination of multiple classifiers by vote)
Chanzo asilia	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Majina mbadala	AL-stacking, query-by-committee stacking, active stacked generalization, stacking with active query	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Zinazohusiana	5	5
Muhtasari	Active Learning Stacking Ensemble combines an active learning query loop with stacked generalization: a pool of unlabeled data is available, and the model iteratively selects the most informative instances for human labeling, using those labels to train and refine a stacking ensemble of multiple base learners topped by a meta-learner. This approach reduces annotation cost while maximizing the predictive power of the ensemble.	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.