방법 비교

	앙상블 능동 학습	Voting Ensemble
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	1992	1990s–2004
창시자	Seung, H. S., Opper, M., & Sompolinsky, H.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
유형	Ensemble-based active learning strategy	Ensemble (combination of multiple classifiers by vote)
원전	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
별칭	Query by Committee, QBC active learning, committee-based active learning, ensemble query strategy	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
관련	5	5
요약	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.