পদ্ধতির তুলনা করুন

	অনলাইন অ্যাক্টিভ লার্নিং	Online Random Forest
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2000s	2009
প্রবর্তক	Cesa-Bianchi, N. and others (multiple contributors)	Saffari, A. et al.
ধরন	Hybrid learning paradigm (online + active)	Incremental ensemble (streaming decision trees)
মৌলিক উৎস	Cesa-Bianchi, N., Gentile, C., & Zaniboni, L. (2006). Worst-case analysis of selective sampling for linear classification. Journal of Machine Learning Research, 7, 1205–1230.	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.
অপর নাম	streaming active learning, online query-by-committee, sequential active learning, incremental active learning	ORF, streaming random forest, incremental random forest, adaptive random forest
সম্পর্কিত	6	6
সারসংক্ষেপ	Online active learning combines two complementary paradigms: it processes data as a stream (online learning) and selectively requests labels only for the most informative instances (active learning). The result is a model that adapts continuously to new data while keeping labeling costs low — useful whenever labeled data is expensive and examples arrive sequentially rather than all at once.	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.