Comparar métodos

	Bagging Semi-supervisionado	Propagação de Rótulos
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2000s	2002
Autor original	Various (Breiman bagging + semi-supervised extensions, 1990s–2000s)	Zhu, X. & Ghahramani, Z.
Tipo	Semi-supervised ensemble (bagging variant)	Graph-based semi-supervised classification
Fonte seminal	Bennett, K. P., & Demiriz, A. (1999). Semi-supervised support vector machines. Advances in Neural Information Processing Systems, 11. MIT Press.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
Outros nomes	SS-Bagging, semi-supervised bootstrap aggregating, self-training bagging, bagging with pseudo-labels	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Relacionados	4	3
Resumo	Semi-supervised Bagging extends the classical bagging ensemble to settings where labeled training examples are scarce but large amounts of unlabeled data are available. Base learners trained on labeled data assign pseudo-labels to unlabeled examples; the expanded dataset is then used to grow a diverse ensemble whose aggregated vote is more accurate and more stable than any single model trained on the limited labeled set 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.