قارن الطرق

	تعلم النقل الجماعي	التعزيز
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	2010s	1990–1997
صاحب الطريقة	Various (consolidated in deep learning era, 2010s)	Schapire, R. E.; Freund, Y.
النوع	Ensemble of pre-trained / fine-tuned models	Sequential ensemble (iterative reweighting)
المصدر التأسيسي	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.
الأسماء البديلة	transfer ensemble, multi-model transfer learning, ensemble of fine-tuned models, ETL	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
ذات صلة	6	6
الملخص	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.