Módszerek összehasonlítása

	Ensemble Transzfer Tanulás	Boosting
Tudományterület	Gépi tanulás	Gépi tanulás
Módszercsalád	Machine learning	Machine learning
Keletkezés éve	2010s	1990–1997
Megalkotó	Various (consolidated in deep learning era, 2010s)	Schapire, R. E.; Freund, Y.
Típus	Ensemble of pre-trained / fine-tuned models	Sequential ensemble (iterative reweighting)
Alapmű	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.
Alternatív nevek	transfer ensemble, multi-model transfer learning, ensemble of fine-tuned models, ETL	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Kapcsolódó	6	6
Összefoglaló	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.