Порівняння методів

	Ансамбль голосування активного навчання	Бустинг
Галузь	Машинне навчання	Машинне навчання
Родина	Machine learning	Machine learning
Рік появи	1992	1990–1997
Автор методу	Seung, H. S., Opper, M., & Sompolinsky, H.	Schapire, R. E.; Freund, Y.
Тип	Active learning with ensemble voting	Sequential ensemble (iterative reweighting)
Основоположне джерело	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.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.
Інші назви	Query by Committee, QBC, active ensemble learning, committee-based active learning	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Пов'язані	5	6
Підсумок	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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.