Сравнение на методи

	Label Propagation	Полу-наблюдавано обучение
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	2002	1970s–2006 (formalized)
Създател	Zhu, X. & Ghahramani, Z.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Тип	Graph-based semi-supervised classification	Learning paradigm
Основополагащ източник	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Други названия	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Свързани	3	5
Резюме	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.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.