Comparer des méthodes

	Apprentissage par transfert régularisé	Forêt Aléatoire Régularisée
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000s–2010s	2012
Auteur d'origine	Pan, S. J. & Yang, Q. (survey); regularization variants by multiple authors	Deng, H. & Runger, G.
Type	Regularized supervised/semi-supervised learning framework	Regularized ensemble (penalized feature selection in trees)
Source fondatrice	Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.	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	regularized domain adaptation, transfer learning with regularization, penalized transfer learning, regularized fine-tuning	RRF, Guided Regularized Random Forest, GRRF, regularized tree ensemble
Apparentées	6	5
Résumé	Regularized Transfer Learning applies explicit penalty terms to a transfer learning pipeline to control how much a model shifts away from source-domain knowledge when adapting to a new target domain. The regularizer discourages negative transfer — the harmful carry-over of irrelevant source patterns — while preserving beneficial shared representations and preventing overfitting when target-domain labels 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.