Usporedite metode

	Online Boosting	Boosting	Povećanje gradijenta
Područje	Strojno učenje	Strojno učenje	Strojno učenje
Obitelj	Machine learning	Machine learning	Machine learning
Godina nastanka	2001	1990–1997	2001
Tvorac	Oza, N. C. & Russell, S.	Schapire, R. E.; Freund, Y.	Friedman, J. H.
Vrsta	Online ensemble (incremental boosting)	Sequential ensemble (iterative reweighting)	Ensemble (sequential boosting of decision trees)
Temeljni izvor	Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann.	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.
Drugi nazivi	streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Srodne	6	6	5
Sažetak	Online Boosting adapts the classical boosting framework to data streams, updating an ensemble of weak learners one example at a time without storing the full dataset. The Oza-Russell formulation approximates AdaBoost's reweighting using Poisson-sampled instance counts, enabling accurate, adaptive classification in real-time or resource-constrained environments.	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.	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.