Porovnat metody

	Propagace popisků	XGBoost
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	2002	2016
Tvůrce	Zhu, X. & Ghahramani, Z.	Chen, T. & Guestrin, C.
Typ	Graph-based semi-supervised classification	Ensemble (gradient-boosted decision trees)
Původní zdroj	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Další názvy	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation	XGBoost, extreme gradient boosting, scalable tree boosting
Příbuzné	3	5
Shrnutí	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.