পদ্ধতির তুলনা করুন

	অনলাইন বুস্টিং	Online Random Forest
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2001	2009
প্রবর্তক	Oza, N. C. & Russell, S.	Saffari, A. et al.
ধরন	Online ensemble (incremental boosting)	Incremental ensemble (streaming decision trees)
মৌলিক উৎস	Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann.	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 boosting, incremental boosting, online AdaBoost, online ensemble boosting	ORF, streaming random forest, incremental random forest, adaptive random forest
সম্পর্কিত	6	6
সারসংক্ষেপ	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 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.