Сравнение методов

	Полуавтоматический ансамбль голосования	Бустинг
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1998–2005	1990–1997
Автор метода	Zhou, Z.-H. & Li, M. (tri-training); Blum & Mitchell (co-training)	Schapire, R. E.; Freund, Y.
Тип	Semi-supervised ensemble (voting)	Sequential ensemble (iterative reweighting)
Основополагающий источник	Zhou, Z.-H., & Li, M. (2005). Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 17(11), 1529–1541.	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.
Другие названия	semi-supervised majority vote, SSL voting ensemble, co-training voting classifier, semi-supervised multi-classifier voting	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Связанные	5	6
Сводка	A semi-supervised voting ensemble trains multiple classifiers on a small labeled set, then iteratively exploits unlabeled data by having the classifiers label examples they agree on, expanding the training pool until all classifiers vote jointly on test examples. It combines the label-efficiency of semi-supervised learning with the variance-reduction of majority-vote ensembles, making it valuable when annotation is costly.	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.