Sammenlign metoder

	Regulariseret semisuperviseret læring	Label Propagation
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2006	2002
Ophavsperson	Belkin, M.; Niyogi, P.; Sindhwani, V.	Zhu, X. & Ghahramani, Z.
Type	Regularized learning paradigm	Graph-based semi-supervised classification
Oprindelig kilde	Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7, 2399–2434.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
Aliasser	manifold regularization, graph-regularized SSL, semi-supervised regularization, Laplacian regularization	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Relaterede	6	3
Resumé	Regularized semi-supervised learning adds explicit geometric or graph-based penalty terms to a semi-supervised objective so that the decision function varies smoothly over the data manifold. Pioneered through manifold regularization (Belkin, Niyogi & Sindhwani, 2006), it exploits the structure of both labeled and unlabeled examples to learn more accurate models than supervised regularization 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.