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	Semi-supervised XGBoost	勾配ブースティング	ラベル伝播
分野	機械学習	機械学習	機械学習
系統	Machine learning	Machine learning	Machine learning
提唱年	2016–2018	2001	2002
提唱者	Chen, T. & Guestrin, C. (XGBoost); semi-supervised extension by multiple authors	Friedman, J. H.	Zhu, X. & Ghahramani, Z.
種類	Ensemble (semi-supervised gradient boosting)	Ensemble (sequential boosting of decision trees)	Graph-based semi-supervised classification
原典	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.	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.
別名	SS-XGBoost, semi-supervised gradient boosting, pseudo-label XGBoost, label-propagation XGBoost	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
関連	4	5	3
概要	Semi-supervised XGBoost extends the XGBoost gradient boosting framework to settings where only a fraction of training examples carry labels. By iteratively generating pseudo-labels for unlabeled data and retraining on the expanded set, the method extracts signal from unlabeled observations, improving generalization when labeled data are scarce.	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.