Confronta i metodi

	Gradient Boosting	Boosting Semi-Supervisionato
Campo	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning
Anno di origine	2001	1999–2009
Ideatore	Friedman, J. H.	Mallapragada, P. K.; Bennett, K. P.; and others
Tipo	Ensemble (sequential boosting of decision trees)	Semi-supervised ensemble method
Fonte seminale	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	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.
Alias	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting
Correlati	5	5
Sintesi	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.	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.