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| Uczenie transferowe z wykorzystaniem zespołów× | Random Forest× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2010s | 2001 |
| Twórca≠ | Various (consolidated in deep learning era, 2010s) | Breiman, L. |
| Typ≠ | Ensemble of pre-trained / fine-tuned models | Ensemble (bagging of decision trees) |
| Źródło pierwotne≠ | 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 ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Inne nazwy | transfer ensemble, multi-model transfer learning, ensemble of fine-tuned models, ETL | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Pokrewne≠ | 6 | 4 |
| Podsumowanie≠ | 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. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
| ScholarGateZbiór danych ↗ |
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