方法对比

	在线提升 (Online Boosting)	在线Bagging	在线学习
领域	机器学习	机器学习	机器学习
方法族	Machine learning	Machine learning	Machine learning
起源年份	2001	2001	1958–2000s
提出者	Oza, N. C. & Russell, S.	Oza, N. C. & Russell, S.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
类型	Online ensemble (incremental boosting)	Online ensemble (streaming bagging)	Learning paradigm (sequential model update)
开创性文献	Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann.	Oza, N. C., & Russell, S. (2001). Online bagging and boosting. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001), pp. 105–112.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
别名	streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting	incremental bagging, streaming bagging, online bootstrap aggregating, OzaBag	incremental learning, sequential learning, streaming learning, online machine learning
相关	6	4	6
摘要	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 Bagging is a streaming ensemble method introduced by Oza and Russell in 2001 that adapts the classical bootstrap aggregating (Bagging) framework to the online learning setting. Instead of resampling a fixed dataset, each incoming instance is fed to every base learner a Poisson(1)-distributed number of times, faithfully approximating bootstrap sampling as the stream evolves. The result is a robust, incrementally updated ensemble that can handle concept drift and continuous data arrival without storing the entire dataset.	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.