Methoden vergleichen

	Ensemble Active Learning	Voting Ensemble
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1992	1990s–2004
Urheber	Seung, H. S., Opper, M., & Sompolinsky, H.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Typ	Ensemble-based active learning strategy	Ensemble (combination of multiple classifiers by vote)
Wegweisende Quelle	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
Aliasnamen	Query by Committee, QBC active learning, committee-based active learning, ensemble query strategy	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Verwandt	5	5
Zusammenfassung	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.