Comparar métodos

	Doc2Vec Semi-supervisionado	Propagação de Rótulos
Área	Aprendizado profundo	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2014–2017	2002
Autor original	Le, Q. V. & Mikolov, T. (base Doc2Vec); semi-supervised extensions by various authors circa 2015–2019	Zhu, X. & Ghahramani, Z.
Tipo	Semi-supervised representation learning	Graph-based semi-supervised classification
Fonte seminal	Le, Q. V., & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on Machine Learning (ICML 2014), PMLR 32(2), 1188–1196.	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	Semi-supervised Paragraph Vector, SS-Doc2Vec, Label-guided PV-DBOW, Semi-supervised PV-DM	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Relacionados	3	3
Resumo	Semi-supervised Doc2Vec extends the Paragraph Vector framework of Le and Mikolov (2014) by training dense document embeddings on both labeled and unlabeled corpora simultaneously, using available class labels as an auxiliary signal to steer the representation toward task-relevant structure while still exploiting the full unlabeled collection for generalization.	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.