Methoden vergleichen

	Active Learning Voting Ensemble	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	Active learning with ensemble voting	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 '92), 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 ensemble learning, committee-based active learning	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Verwandt	5	5
Zusammenfassung	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.