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	Etikettpropagering	XGBoost
Ämnesområde	Maskininlärning	Maskininlärning
Familj	Machine learning	Machine learning
Ursprungsår	2002	2016
Upphovsperson	Zhu, X. & Ghahramani, Z.	Chen, T. & Guestrin, C.
Typ	Graph-based semi-supervised classification	Ensemble (gradient-boosted decision trees)
Ursprungskälla	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.
Alias	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation	XGBoost, extreme gradient boosting, scalable tree boosting
Närliggande	3	5
Sammanfattning	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.