Bandingkan kaedah

	Online Boosting	Pembelajaran Dalam Talian
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	2001	1958–2000s
Pengasas	Oza, N. C. & Russell, S.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Jenis	Online ensemble (incremental boosting)	Learning paradigm (sequential model update)
Sumber perintis	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.
Alias	streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting	incremental learning, sequential learning, streaming learning, online machine learning
Berkaitan	6	6
Ringkasan	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.