Compară metode

	Învățare online semi-supervizată	Propagarea etichetelor
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2000s–2010s	2002
Autorul original	Goldberg, A.; Li, M.; Zhu, X. (among key contributors)	Zhu, X. & Ghahramani, Z.
Tip	Hybrid learning paradigm (online + semi-supervised)	Graph-based semi-supervised classification
Sursa seminală	Goldberg, A., Li, M., & Zhu, X. (2008). Online manifold regularization: A new learning setting and empirical study. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2008), Lecture Notes in Computer Science, 5211, 393–407. Springer.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
Denumiri alternative	SSOL, online semi-supervised learning, semi-supervised incremental learning, streaming semi-supervised learning	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Înrudite	4	3
Rezumat	Semi-supervised Online Learning combines the incremental update style of online learning with the ability to exploit unlabeled examples, enabling models to improve continuously from a data stream in which only a small fraction of arriving instances carry ground-truth labels. It is especially valuable when labeling is expensive or delayed but data arrives in real time.	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.