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| Ensemble Transfer Learning× | Boosting× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2010s | 1990–1997 |
| Urheber≠ | Various (consolidated in deep learning era, 2010s) | Schapire, R. E.; Freund, Y. |
| Typ≠ | Ensemble of pre-trained / fine-tuned models | Sequential ensemble (iterative reweighting) |
| Wegweisende Quelle≠ | 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. DOI ↗ | 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. DOI ↗ |
| Aliasnamen | transfer ensemble, multi-model transfer learning, ensemble of fine-tuned models, ETL | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | 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. |
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