So sánh phương pháp

	Tăng cường Gradient bán giám sát	Random Forest Bán Giám Sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2006–2010s	2009
Người khởi xướng	Chapelle, Scholkopf & Zien (eds.); applied to GBM variants in subsequent literature	Leistner, C., Saffari, A., Santner, J., & Bischof, H.
Loại	Semi-supervised ensemble (self-training + gradient boosted trees)	Semi-supervised ensemble classifier
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.)	Leistner, C., Saffari, A., Santner, J., & Bischof, H. (2009). Semi-supervised random forests. In Proceedings of the IEEE 12th International Conference on Computer Vision (ICCV), pp. 506–513. IEEE.
Tên gọi khác	pseudo-label gradient boosting, self-training GBM, semi-supervised GBT, label-propagation boosting	SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest
Liên quan	6	3
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.	Semi-supervised Random Forest (SSL-RF) extends the classic Random Forest by exploiting both labeled and unlabeled training examples. When labeling data is expensive or time-consuming, SSL-RF assigns tentative pseudo-labels to unlabeled observations through the forest itself, then retrains on the enriched dataset, progressively improving accuracy without requiring additional human annotation.