Comparer des méthodes

	Apprentissage semi-supervisé	Forêt aléatoire semi-supervisée
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1970s–2006 (formalized)	2009
Auteur d'origine	Vapnik, V. N. and others (community of researchers, 1970s–2000s)	Leistner, C., Saffari, A., Santner, J., & Bischof, H.
Type	Learning paradigm	Semi-supervised ensemble classifier
Source fondatrice	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	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	SSL, semi-supervised machine learning, transductive learning, label-efficient learning	SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest
Apparentées	5	3
Résumé	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.	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.