So sánh phương pháp

	Tăng cường Gradient bán giám sát	XGBoost
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2006–2010s	2016
Người khởi xướng	Chapelle, Scholkopf & Zien (eds.); applied to GBM variants in subsequent literature	Chen, T. & Guestrin, C.
Loại	Semi-supervised ensemble (self-training + gradient boosted trees)	Ensemble (gradient-boosted decision trees)
Công trình gốc	Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. Proceedings of ACL 1995, 189–196. (Foundational self-training framework underlying pseudo-label approaches.)	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Tên gọi khác	pseudo-label gradient boosting, self-training GBM, semi-supervised GBT, label-propagation boosting	XGBoost, extreme gradient boosting, scalable tree boosting
Liên quan	6	5
Tóm tắt	Semi-supervised gradient boosting combines gradient boosted trees with self-training or pseudo-labeling to exploit large pools of unlabeled data alongside a small labeled set. An initial GBM fit on labeled data assigns confident predictions to unlabeled examples; those pseudo-labeled points are folded back into training and the model is re-boosted, iterating until convergence. This allows practitioners to harness cheap unlabeled data when labels are scarce or expensive.	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.