Methoden vergleichen

	Label Propagation	Semi-Supervised Learning
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2002	1970s–2006 (formalized)
Urheber	Zhu, X. & Ghahramani, Z.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Typ	Graph-based semi-supervised classification	Learning paradigm
Wegweisende Quelle	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
Aliasnamen	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Verwandt	3	5
Zusammenfassung	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.