Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Propagación de Etiquetas× | XGBoost× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2002 | 2016 |
| Autor original≠ | Zhu, X. & Ghahramani, Z. | Chen, T. & Guestrin, C. |
| Tipo≠ | Graph-based semi-supervised classification | Ensemble (gradient-boosted decision trees) |
| Fuente seminal≠ | Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Alias≠ | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation | XGBoost, extreme gradient boosting, scalable tree boosting |
| Relacionados≠ | 3 | 5 |
| Resumen≠ | Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data. | 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. |
| ScholarGateConjunto de datos ↗ |
|
|