So sánh phương pháp

	Random Forest Bán Giám Sát	Lan truyền nhãn
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2009	2002
Người khởi xướng	Leistner, C., Saffari, A., Santner, J., & Bischof, H.	Zhu, X. & Ghahramani, Z.
Loại	Semi-supervised ensemble classifier	Graph-based semi-supervised classification
Công trình gốc	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.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
Tên gọi khác	SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Liên quan	3	3
Tóm tắt	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.	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.