Salīdzināt metodes

	Loģistikas regresija ar daļēji uzraudzītu apmācību	Iezīmju izplatīšana
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1995–2000	2002
Autors	Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training)	Zhu, X. & Ghahramani, Z.
Tips	Semi-supervised classifier	Graph-based semi-supervised classification
Pirmavots	Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39, 103–134.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
Citi nosaukumi	SSL logistic regression, semi-supervised LR, EM logistic regression, self-training logistic classifier	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Saistītās	5	3
Kopsavilkums	Semi-supervised logistic regression extends the standard logistic classifier by incorporating unlabeled data during training. Using self-training, expectation-maximization, or label-propagation wrappers, it iteratively assigns soft labels to unlabeled examples and refines model parameters, improving generalization when labeled data are scarce relative to the full dataset.	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.