Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Nevral arkitektursøk× | XGBoost× | |
|---|---|---|
| Fagfelt≠ | Dyp læring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Opprinnelsesår≠ | 2017 | 2016 |
| Opphavsperson≠ | Zoph, B. & Le, Q.V. | Chen, T. & Guestrin, C. |
| Type≠ | Automated architecture optimization (deep learning) | Ensemble (gradient-boosted decision trees) |
| Opprinnelig kilde≠ | Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR. link ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Alias≠ | Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search | XGBoost, extreme gradient boosting, scalable tree boosting |
| Relaterte | 5 | 5 |
| Sammendrag≠ | Neural Architecture Search (NAS), introduced by Zoph and Le in 2017, automatically optimizes architectural decisions such as a network's depth, width, and connection structure instead of hand-designing them. Leading methods in the field include DARTS, ENAS, and Once-for-All. | 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. |
| ScholarGateDatasett ↗ |
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