Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Grafu uzmanības tīkls (Graph Attention Network, GAT)× | XGBoost× | |
|---|---|---|
| Nozare≠ | Dziļā mācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2018 | 2016 |
| Autors≠ | Veličković, P. et al. | Chen, T. & Guestrin, C. |
| Tips≠ | Graph neural network (attention-based) | Ensemble (gradient-boosted decision trees) |
| Pirmavots≠ | Veličković, P. et al. (2018). Graph Attention Networks. ICLR. link ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Citi nosaukumi≠ | Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network | XGBoost, extreme gradient boosting, scalable tree boosting |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | The Graph Attention Network (GAT), introduced by Veličković and colleagues in 2018, is a graph neural network variant that learns how much importance to assign to each neighbouring node through a self-attention mechanism. On heterogeneous neighbourhoods and relational classification it produces results superior to graph convolutional networks (GCN). | 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. |
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