Сравнение методов

	Online Random Forest	Онлайн-обучение
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2009	1958–2000s
Автор метода	Saffari, A. et al.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Тип	Incremental ensemble (streaming decision trees)	Learning paradigm (sequential model update)
Основополагающий источник	Saffari, A., Leistner, C., Santner, J., Godec, M., & Bischof, H. (2009). On-line random forests. In Proceedings of the 3rd IEEE International Workshop on On-Line Learning for Computer Vision (OLCV 2009), pp. 1–8. IEEE.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Другие названия	ORF, streaming random forest, incremental random forest, adaptive random forest	incremental learning, sequential learning, streaming learning, online machine learning
Связанные	6	6
Сводка	Online Random Forest (ORF) extends the classic Random Forest to streaming settings, updating each tree incrementally as new observations arrive without storing or replaying the full training set. Algorithms such as Adaptive Random Forests (ARF) add drift detection so the ensemble adapts when the data distribution changes over time.	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.