Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Graafiverkko (Graph Attention Network, GAT)× | Hierarkkinen ryvästyminen× | |
|---|---|---|
| Tieteenala≠ | Syväoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2018 | 1963 |
| Kehittäjä≠ | Veličković, P. et al. | Ward, J. H. |
| Tyyppi≠ | Graph neural network (attention-based) | Unsupervised clustering (agglomerative) |
| Alkuperäislähde≠ | Veličković, P. et al. (2018). Graph Attention Networks. ICLR. link ↗ | Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. DOI ↗ |
| Rinnakkaisnimet≠ | Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network | Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering |
| Liittyvät | 4 | 4 |
| Tiivistelmä≠ | 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). | Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963. |
| ScholarGateAineisto ↗ |
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