قارن الطرق

	تجميع التصويت للتعلم النشط	التصويت التجميعي
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	1992	1990s–2004
صاحب الطريقة	Seung, H. S., Opper, M., & Sompolinsky, H.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
النوع	Active learning with ensemble voting	Ensemble (combination of multiple classifiers by vote)
المصدر التأسيسي	Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory (COLT '92), pp. 287–294. ACM.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
الأسماء البديلة	Query by Committee, QBC, active ensemble learning, committee-based active learning	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
ذات صلة	5	5
الملخص	Active Learning Voting Ensemble — formally known as Query by Committee — is an active learning strategy that trains a committee of diverse models and selects the unlabeled examples where the committee members disagree most for human annotation. By focusing labeling effort on the most informative points, it achieves high accuracy with far fewer labeled examples than passive learning requires.	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.