Comparar métodos

	Online Boosting	Boosting	Aprendizado Online
Área	Aprendizado de máquina	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning	Machine learning
Ano de origem	2001	1990–1997	1958–2000s
Autor original	Oza, N. C. & Russell, S.	Schapire, R. E.; Freund, Y.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Tipo	Online ensemble (incremental boosting)	Sequential ensemble (iterative reweighting)	Learning paradigm (sequential model update)
Fonte seminal	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.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Outros nomes	streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	incremental learning, sequential learning, streaming learning, online machine learning
Relacionados	6	6	6
Resumo	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.	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.