Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Iezīmju izplatīšana× | Daudzpusīgā apguve× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2002 | 1970s–2006 (formalized) |
| Autors≠ | Zhu, X. & Ghahramani, Z. | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Tips≠ | Graph-based semi-supervised classification | Learning paradigm |
| Pirmavots≠ | Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Citi nosaukumi | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Saistītās≠ | 3 | 5 |
| Kopsavilkums≠ | 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. |
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