Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Utafutaji wa Usanifu wa Neural× | XGBoost× | |
|---|---|---|
| Nyanja≠ | Ujifunzaji wa Kina | Ujifunzaji wa Mashine |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2017 | 2016 |
| Mwanzilishi≠ | Zoph, B. & Le, Q.V. | Chen, T. & Guestrin, C. |
| Aina≠ | Automated architecture optimization (deep learning) | Ensemble (gradient-boosted decision trees) |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala≠ | Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search | XGBoost, extreme gradient boosting, scalable tree boosting |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | 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. |
| ScholarGateSeti ya data ↗ |
|
|