Linganisha mbinu

	Ujifunzaji Amilifu wa Vikundi (Ensemble Active Learning)	Kikundi cha Kura (Voting Ensemble)
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1992	1990s–2004
Mwanzilishi	Seung, H. S., Opper, M., & Sompolinsky, H.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Aina	Ensemble-based active learning strategy	Ensemble (combination of multiple classifiers by vote)
Chanzo asilia	Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory (COLT 1992), pp. 287–294. ACM.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Majina mbadala	Query by Committee, QBC active learning, committee-based active learning, ensemble query strategy	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Zinazohusiana	5	5
Muhtasari	Ensemble Active Learning combines a committee of diverse models with an active learning loop to select the most informative unlabeled examples for labeling. Rooted in the Query by Committee framework introduced by Seung et al. (1992), it uses disagreement among committee members as a signal for uncertainty, reducing the number of labeled examples needed to achieve strong predictive performance.	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.