Módszerek összehasonlítása
Tekintse át a kiválasztott módszereket egymás mellett; az eltérő sorok kiemelve jelennek meg.
| Szakértők keveréke× | Véletlen erdő× | |
|---|---|---|
| Tudományterület≠ | Mélytanulás | Gépi tanulás |
| Módszercsalád | Machine learning | Machine learning |
| Keletkezés éve≠ | 2017 | 2001 |
| Megalkotó≠ | Shazeer, N. et al. | Breiman, L. |
| Típus≠ | Sparse neural network architecture (conditional computation) | Ensemble (bagging of decision trees) |
| Alapmű≠ | Shazeer, N. et al. (2017). Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. ICLR. arXiv:1701.06538 link ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Alternatív nevek≠ | Uzman Karışımı (Mixture of Experts — MoE), uzman karışımı, MoE, sparse mixture of experts | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Kapcsolódó≠ | 3 | 4 |
| Összefoglaló≠ | Mixture of Experts (MoE) is a sparse neural-network architecture, introduced by Shazeer and colleagues in 2017 with the sparsely-gated MoE layer, in which only a subset of expert sub-networks is activated for each input. As seen in models such as Switch Transformer and Mixtral, it holds computation cost fixed even as the total parameter count grows. | 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. |
| ScholarGateAdatkészlet ↗ |
|
|