Сравнение методов

	Полуавтоматическое бустирование (Semi-supervised Boosting)	Градиентный бустинг
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1999–2009	2001
Автор метода	Mallapragada, P. K.; Bennett, K. P.; and others	Friedman, J. H.
Тип	Semi-supervised ensemble method	Ensemble (sequential boosting of decision trees)
Основополагающий источник	Mallapragada, P. K., Jin, R., Jain, A. K., & Liu, Y. (2009). SemiBoost: Boosting for Semi-supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11), 2000–2014.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Другие названия	SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Связанные	5	5
Сводка	Semi-supervised Boosting is an ensemble learning paradigm that extends classical boosting algorithms — such as AdaBoost — to exploit both labeled and unlabeled data. By propagating label information through a similarity structure over unlabeled instances, it trains stronger classifiers than supervised boosting alone when labeled data are scarce.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.