Comparer des méthodes

	Modèle gaussien de mélange semi-supervisé	Propagation d'étiquettes
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000	2002
Auteur d'origine	Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.	Zhu, X. & Ghahramani, Z.
Type	Generative semi-supervised classifier	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	SS-GMM, semi-supervised GMM, partially labeled Gaussian mixture model, generative semi-supervised classifier	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Apparentées	3	3
Résumé	The Semi-supervised Gaussian Mixture Model (SS-GMM) is a generative probabilistic classifier that fits a Gaussian mixture to both labeled and unlabeled data using the Expectation-Maximization algorithm. Labeled points constrain component assignments while unlabeled points improve density estimates, enabling effective learning when annotations are scarce.	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.