Comparer des méthodes

	Forêt aléatoire semi-supervisée	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2009	2001
Auteur d'origine	Leistner, C., Saffari, A., Santner, J., & Bischof, H.	Breiman, L.
Type	Semi-supervised ensemble classifier	Ensemble (bagging of decision trees)
Source fondatrice	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	3	4
Résumé	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.