Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Grafneuralnätverk× | Support Vector Machine (Klassificering)× | XGBoost× | |
|---|---|---|---|
| Ämnesområde≠ | Djupinlärning | Maskininlärning | Maskininlärning |
| Familj | Machine learning | Machine learning | Machine learning |
| Ursprungsår≠ | 2017 | 1995 | 2016 |
| Upphovsperson≠ | Kipf, T.N. & Welling, M. | Cortes, C. & Vapnik, V. | Chen, T. & Guestrin, C. |
| Typ≠ | Deep learning on graph-structured data | Maximum-margin classifier (kernel method) | Ensemble (gradient-boosted decision trees) |
| Ursprungskälla≠ | 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 ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. 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 | XGBoost, extreme gradient boosting, scalable tree boosting |
| Närliggande≠ | 4 | 5 | 5 |
| Sammanfattning≠ | 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. | XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions. |
| ScholarGateDatamängd ↗ |
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