Comparer des méthodes

	Apprentissage semi-supervisé régularisé	Forêt Aléatoire Régularisée
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2006	2012
Auteur d'origine	Belkin, M.; Niyogi, P.; Sindhwani, V.	Deng, H. & Runger, G.
Type	Regularized learning paradigm	Regularized ensemble (penalized feature selection in trees)
Source fondatrice	Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7, 2399–2434.	Deng, H., & Runger, G. (2012). Feature selection via regularized trees. Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1–8.
Alias	manifold regularization, graph-regularized SSL, semi-supervised regularization, Laplacian regularization	RRF, Guided Regularized Random Forest, GRRF, regularized tree ensemble
Apparentées	6	5
Résumé	Regularized semi-supervised learning adds explicit geometric or graph-based penalty terms to a semi-supervised objective so that the decision function varies smoothly over the data manifold. Pioneered through manifold regularization (Belkin, Niyogi & Sindhwani, 2006), it exploits the structure of both labeled and unlabeled examples to learn more accurate models than supervised regularization alone when labeled data are scarce.	Regularized Random Forest (RRF), introduced by Deng and Runger in 2012, extends the standard Random Forest by adding a penalty that discourages splits on features not already used in the ensemble. This built-in regularization produces sparser, less redundant feature subsets, making the model especially valuable when feature selection is as important as predictive accuracy.