Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Classificação de Imagens por CNN× | Random Forest× | |
|---|---|---|
| Área≠ | Aprendizado profundo | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 2016 | 2001 |
| Autor original≠ | He, K. et al. (ResNet); Tan, M. & Le, Q.V. (EfficientNet) | Breiman, L. |
| Tipo≠ | Deep convolutional neural network (supervised) | Ensemble (bagging of decision trees) |
| Fonte seminal≠ | He, K., Zhang, X., Ren, S. & Sun, J. (2016). Deep Residual Learning for Image Recognition. CVPR. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Outros nomes | CNN — Görüntü Sınıflandırma (ResNet / VGG / EfficientNet), convolutional neural network image classifier, deep image classification, ResNet / VGG / EfficientNet | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Relacionados≠ | 5 | 4 |
| Resumo≠ | CNN image classification uses deep convolutional architectures such as ResNet (He et al., 2016), VGG and EfficientNet (Tan & Le, 2019) to sort images into categories. Stacked convolutional layers learn a hierarchy of visual features directly from pixels, and skip (residual) connections prevent the vanishing-gradient problem in very deep networks. | 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. |
| ScholarGateConjunto de dados ↗ |
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