So sánh phương pháp

	Tăng cường Gradient bán giám sát	Boosting
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2006–2010s	1990–1997
Người khởi xướng	Chapelle, Scholkopf & Zien (eds.); applied to GBM variants in subsequent literature	Schapire, R. E.; Freund, Y.
Loại	Semi-supervised ensemble (self-training + gradient boosted trees)	Sequential ensemble (iterative reweighting)
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.)	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.
Tên gọi khác	pseudo-label gradient boosting, self-training GBM, semi-supervised GBT, label-propagation boosting	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Liên quan	6	6
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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.