Comparer des méthodes

	Forêt aléatoire en ligne	Forêt aléatoire semi-supervisée
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2009	2009
Auteur d'origine	Saffari, A. et al.	Leistner, C., Saffari, A., Santner, J., & Bischof, H.
Type	Incremental ensemble (streaming decision trees)	Semi-supervised ensemble classifier
Source fondatrice	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.
Alias	ORF, streaming random forest, incremental random forest, adaptive random forest	SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest
Apparentées	6	3
Résumé	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.