Comparer des méthodes

	Processus Gaussien semi-supervisé	Forêt aléatoire semi-supervisée
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2004	2009
Auteur d'origine	Lawrence, N. D. & Jordan, M. I.	Leistner, C., Saffari, A., Santner, J., & Bischof, H.
Type	Probabilistic model (semi-supervised)	Semi-supervised ensemble classifier
Source fondatrice	Lawrence, N. D., & Jordan, M. I. (2004). Semi-supervised learning via Gaussian processes. In Advances in Neural Information Processing Systems (NIPS), 17, 753–760. MIT Press.	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	SS-GP, semi-supervised GP, Gaussian process with unlabeled data, GP manifold learning	SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest
Apparentées	5	3
Résumé	Semi-supervised Gaussian Process extends the probabilistic GP framework to exploit unlabeled data alongside a small set of labeled observations. By placing a GP prior over functions and leveraging the geometric structure revealed by unlabeled inputs, it learns more accurate and better-calibrated predictors than a purely supervised GP when labels are scarce, making it well suited for scientific and medical problems where annotation is expensive.	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.