השוואת שיטות

	למידת העברה מצרפית	בוסטינג
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2010s	1990–1997
הוגה השיטה	Various (consolidated in deep learning era, 2010s)	Schapire, R. E.; Freund, Y.
סוג	Ensemble of pre-trained / fine-tuned models	Sequential ensemble (iterative reweighting)
מקור מכונן	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.
כינויים	transfer ensemble, multi-model transfer learning, ensemble of fine-tuned models, ETL	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
קשורות	6	6
תקציר	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.