Salīdzināt metodes

	Tiešsaistes apguve	Tiešsaistes nejaušais mežs
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1958–2000s	2009
Autors	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)	Saffari, A. et al.
Tips	Learning paradigm (sequential model update)	Incremental ensemble (streaming decision trees)
Pirmavots	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.	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.
Citi nosaukumi	incremental learning, sequential learning, streaming learning, online machine learning	ORF, streaming random forest, incremental random forest, adaptive random forest
Saistītās	6	6
Kopsavilkums	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.	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.