Comparar métodos

	Máquina de Vectores de Soporte Semisupervisada	Propagación de Etiquetas
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	1999	2002
Autor original	Joachims, T.	Zhu, X. & Ghahramani, Z.
Tipo	Semi-supervised classifier	Graph-based semi-supervised classification
Fuente seminal	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
Relacionados	4	3
Resumen	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.