Comparer des méthodes

	Ensemble Transfer Learning	Boosting
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2010s	1990–1997
Auteur d'origine	Various (consolidated in deep learning era, 2010s)	Schapire, R. E.; Freund, Y.
Type	Ensemble of pre-trained / fine-tuned models	Sequential ensemble (iterative reweighting)
Source fondatrice	Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., & Suganthan, P. N. (2022). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, 105151.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.
Alias	transfer ensemble, multi-model transfer learning, ensemble of fine-tuned models, ETL	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Apparentées	6	6
Résumé	Ensemble Transfer Learning combines multiple models that were each pre-trained on a large source domain and then fine-tuned on a target task. By aggregating the predictions of several independently fine-tuned models, it achieves higher accuracy and robustness than any single transferred model alone, especially when the target dataset is small.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.