Compara mètodes

	Online Boosting	Aprenentatge en línia
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	2001	1958–2000s
Autor original	Oza, N. C. & Russell, S.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Tipus	Online ensemble (incremental boosting)	Learning paradigm (sequential model update)
Font seminal	Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Àlies	streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting	incremental learning, sequential learning, streaming learning, online machine learning
Relacionats	6	6
Resum	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.	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.