Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Réseau de neurones à graphes× | Machine à vecteurs de support (Classification)× | |
|---|---|---|
| Domaine≠ | Apprentissage profond | Apprentissage automatique |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 2017 | 1995 |
| Auteur d'origine≠ | Kipf, T.N. & Welling, M. | Cortes, C. & Vapnik, V. |
| Type≠ | Deep learning on graph-structured data | Maximum-margin classifier (kernel method) |
| Source fondatrice≠ | Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR. link ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Alias | Grafik Sinir Ağı (GNN), GNN, graph neural net, graph convolutional network | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Apparentées≠ | 4 | 5 |
| Résumé≠ | 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. | 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. |
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