Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Графовая нейронная сеть× | Классификация изображений с помощью свёрточных нейронных сетей (CNN)× | Метод опорных векторов (классификация)× | |
|---|---|---|---|
| Область≠ | Глубокое обучение | Глубокое обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning | Machine learning |
| Год появления≠ | 2017 | 2016 | 1995 |
| Автор метода≠ | Kipf, T.N. & Welling, M. | He, K. et al. (ResNet); Tan, M. & Le, Q.V. (EfficientNet) | Cortes, C. & Vapnik, V. |
| Тип≠ | Deep learning on graph-structured data | Deep convolutional neural network (supervised) | Maximum-margin classifier (kernel method) |
| Основополагающий источник≠ | Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR. link ↗ | He, K., Zhang, X., Ren, S. & Sun, J. (2016). Deep Residual Learning for Image Recognition. CVPR. DOI ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Другие названия | Grafik Sinir Ağı (GNN), GNN, graph neural net, graph convolutional network | CNN — Görüntü Sınıflandırma (ResNet / VGG / EfficientNet), convolutional neural network image classifier, deep image classification, ResNet / VGG / EfficientNet | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Связанные≠ | 4 | 5 | 5 |
| Сводка≠ | A Graph Neural Network (GNN) is a deep learning method, popularised by Kipf and Welling in 2017 with the Graph Convolutional Network, that learns from the relationships in network (graph) structures made of nodes and edges. It is designed for data that is naturally relational, such as social networks, molecular structures, and recommendation systems. | 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. | The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data. |
| ScholarGateНабор данных ↗ |
|
|
|