Comparar métodos

	Semi-supervised Boosting	Propagación de Etiquetas
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	1999–2009	2002
Autor original	Mallapragada, P. K.; Bennett, K. P.; and others	Zhu, X. & Ghahramani, Z.
Tipo	Semi-supervised ensemble method	Graph-based semi-supervised classification
Fuente seminal	Mallapragada, P. K., Jin, R., Jain, A. K., & Liu, Y. (2009). SemiBoost: Boosting for Semi-supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11), 2000–2014.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
Alias	SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Relacionados	5	3
Resumen	Semi-supervised Boosting is an ensemble learning paradigm that extends classical boosting algorithms — such as AdaBoost — to exploit both labeled and unlabeled data. By propagating label information through a similarity structure over unlabeled instances, it trains stronger classifiers than supervised boosting alone when labeled data 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.