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	Gradient Boosting	Etikettpropagering	Semi-övervakad inlärning
Ämnesområde	Maskininlärning	Maskininlärning	Maskininlärning
Familj	Machine learning	Machine learning	Machine learning
Ursprungsår	2001	2002	1970s–2006 (formalized)
Upphovsperson	Friedman, J. H.	Zhu, X. & Ghahramani, Z.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Typ	Ensemble (sequential boosting of decision trees)	Graph-based semi-supervised classification	Learning paradigm
Ursprungskälla	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Alias	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Närliggande	5	3	5
Sammanfattning	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.	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.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.