Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Kapsulu tīkls× | Random Forest× | |
|---|---|---|
| Nozare≠ | Dziļā mācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2017 | 2001 |
| Autors≠ | Sabour, S., Frosst, N. & Hinton, G. E. | Breiman, L. |
| Tips≠ | Deep learning architecture (vector capsules with dynamic routing) | Ensemble (bagging of decision trees) |
| Pirmavots≠ | Sabour, S., Frosst, N. & Hinton, G. E. (2017). Dynamic Routing Between Capsules. Advances in Neural Information Processing Systems (NeurIPS). link ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Citi nosaukumi | Kapsül Ağı (CapsNet), CapsNet, capsule net, dynamic routing network | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | A Capsule Network (CapsNet) is a deep learning architecture introduced by Sara Sabour, Nicholas Frosst and Geoffrey Hinton in 2017 that organises neurons as vectors (capsules) rather than scalar activations, so that spatial hierarchy and pose (orientation) information are encoded directly. It was proposed to overcome the fragility of convolutional networks to changes in viewpoint. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
| ScholarGateDatu kopa ↗ |
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