Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Pusgadīgi K tuvāko kaimiņu metode× | Daudzpusīgā apguve× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2002 (semi-supervised extension); 1967 (KNN base) | 1970s–2006 (formalized) |
| Autors≠ | Zhu, X. & Ghahramani, Z. (label propagation); Cover, T. & Hart, P. (KNN base) | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Tips≠ | Semi-supervised classifier / label propagation | 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 | SS-KNN, semi-supervised KNN, KNN label propagation, graph-based semi-supervised KNN | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | Semi-supervised KNN extends the classic K-nearest neighbors algorithm to exploit large pools of unlabeled data alongside a small labeled set. By building a KNN graph over all observations and propagating known labels through the graph's edges, the method infers labels for unlabeled points without requiring expensive manual annotation of every sample. | 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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