So sánh phương pháp

	Hồi quy tuyến tính bán giám sát	Lan truyền nhãn
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2005–2006	2002
Người khởi xướng	Chapelle, O.; Scholkopf, B.; Zien, A. (seminal synthesis); Zhou & Li (co-training formulation)	Zhu, X. & Ghahramani, Z.
Loại	Semi-supervised regression model	Graph-based semi-supervised classification
Công trình gốc	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	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 linear regression, semi-supervised least squares, transductive linear regression, label-efficient linear regression	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Liên quan	4	3
Tóm tắt	Semi-supervised linear regression fits a linear model on a small labeled dataset and then leverages a larger pool of unlabeled observations to improve coefficient estimates and generalization. By generating pseudo-labels for unlabeled points and iteratively refining the model, it achieves better predictive accuracy than a purely supervised linear model trained on scarce labels alone.	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.