Uporedite metode

	Polu-nadgledano pojačavanje	Градијентно појачање
Oblast	Mašinsko učenje	Mašinsko učenje
Porodica	Machine learning	Machine learning
Godina nastanka	1999–2009	2001
Tvorac	Mallapragada, P. K.; Bennett, K. P.; and others	Friedman, J. H.
Tip	Semi-supervised ensemble method	Ensemble (sequential boosting of decision trees)
Temeljni izvor	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.
Drugi nazivi	SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Srodne	5	5
Sažetak	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.