Salīdzināt metodes

	Tiešsaistes pastiprināšana (Online Boosting)	Gradient Boosting	Tiešsaistes apguve
Nozare	Mašīnmācīšanās	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning	Machine learning
Izcelsmes gads	2001	2001	1958–2000s
Autors	Oza, N. C. & Russell, S.	Friedman, J. H.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Tips	Online ensemble (incremental boosting)	Ensemble (sequential boosting of decision trees)	Learning paradigm (sequential model update)
Pirmavots	Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Citi nosaukumi	streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	incremental learning, sequential learning, streaming learning, online machine learning
Saistītās	6	5	6
Kopsavilkums	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.	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.	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.