Comparer des méthodes

	Machine à vecteurs de support semi-supervisée	Propagation d'étiquettes
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1999	2002
Auteur d'origine	Joachims, T.	Zhu, X. & Ghahramani, Z.
Type	Semi-supervised classifier	Graph-based semi-supervised classification
Source fondatrice	Joachims, T. (1999). Transductive Inference for Text Classification using Support Vector Machines. Proceedings of the 16th International Conference on Machine Learning (ICML), 200–209.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
Alias	S3VM, Transductive SVM, TSVM, Semi-SVM	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Apparentées	4	3
Résumé	Semi-supervised Support Vector Machine (S3VM) extends the classical SVM by incorporating large quantities of unlabeled data alongside a small labeled training set. It seeks a maximum-margin hyperplane that not only separates the labeled examples but also passes through low-density regions of the full data distribution, yielding better generalization when labeled samples 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.