Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Rete Neurale Convoluzionale (Classificazione)× | Random Forest× | |
|---|---|---|
| Campo≠ | Apprendimento profondo | Apprendimento automatico |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 1998 | 2001 |
| Ideatore≠ | LeCun, Y. et al. | Breiman, L. |
| Tipo≠ | Deep neural network (convolutional) | Ensemble (bagging of decision trees) |
| Fonte seminale≠ | LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278–2324. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Alias | CNN (Evrişimli Sinir Ağı — Sınıflandırma), CNN classification, ConvNet, convolutional network classifier | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Correlati≠ | 5 | 4 |
| Sintesi≠ | A Convolutional Neural Network (CNN) is a deep learning model, established by LeCun and colleagues in 1998, that learns local patterns directly from images and structured data to classify them. Stacks of convolutional filters discover increasingly abstract features, so manual feature engineering can be largely reduced. | 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. |
| ScholarGateInsieme di dati ↗ |
|
|