Comparer des méthodes

	Ensemble Transfer Learning	Ensemble par vote
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2010s	1990s–2004
Auteur d'origine	Various (consolidated in deep learning era, 2010s)	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Type	Ensemble of pre-trained / fine-tuned models	Ensemble (combination of multiple classifiers by vote)
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.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Alias	transfer ensemble, multi-model transfer learning, ensemble of fine-tuned models, ETL	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Apparentées	6	5
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.	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.