Sammenlign metoder

	Semi-supervised Boosting	AdaBoost	Gradient Boosting
Fagområde	Maskinlæring	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning	Machine learning
Oprindelsesår	1999–2009	1997	2001
Ophavsperson	Mallapragada, P. K.; Bennett, K. P.; and others	Freund, Y. & Schapire, R.E.	Friedman, J. H.
Type	Semi-supervised ensemble method	Ensemble (sequential boosting of weak learners)	Ensemble (sequential boosting of decision trees)
Oprindelig kilde	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.	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.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Aliasser	SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Relaterede	5	5	5
Resumé	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.	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.	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.