পদ্ধতির তুলনা করুন

	Online Random Forest	Semi-supervised Random Forest
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2009	2009
প্রবর্তক	Saffari, A. et al.	Leistner, C., Saffari, A., Santner, J., & Bischof, H.
ধরন	Incremental ensemble (streaming decision trees)	Semi-supervised ensemble classifier
মৌলিক উৎস	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.	Leistner, C., Saffari, A., Santner, J., & Bischof, H. (2009). Semi-supervised random forests. In Proceedings of the IEEE 12th International Conference on Computer Vision (ICCV), pp. 506–513. IEEE.
অপর নাম	ORF, streaming random forest, incremental random forest, adaptive random forest	SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest
সম্পর্কিত	6	3
সারসংক্ষেপ	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.	Semi-supervised Random Forest (SSL-RF) extends the classic Random Forest by exploiting both labeled and unlabeled training examples. When labeling data is expensive or time-consuming, SSL-RF assigns tentative pseudo-labels to unlabeled observations through the forest itself, then retrains on the enriched dataset, progressively improving accuracy without requiring additional human annotation.