Comparer des méthodes

	Régression linéaire semi-supervisée	Propagation d'étiquettes
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2005–2006	2002
Auteur d'origine	Chapelle, O.; Scholkopf, B.; Zien, A. (seminal synthesis); Zhou & Li (co-training formulation)	Zhu, X. & Ghahramani, Z.
Type	Semi-supervised regression model	Graph-based semi-supervised classification
Source fondatrice	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.
Alias	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
Apparentées	4	3
Résumé	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.