Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzpusīgā apguve× | Mācīšanās ar maziem paraugu skaitu× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1970s–2006 (formalized) | 2011–2017 |
| Autors≠ | Vapnik, V. N. and others (community of researchers, 1970s–2000s) | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| Tips≠ | Learning paradigm | Meta-learning / low-data learning paradigm |
| Pirmavots≠ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 | Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., & Kavukcuoglu, K. (2016). Matching Networks for One Shot Learning. Advances in Neural Information Processing Systems (NeurIPS), 29. link ↗ |
| Citi nosaukumi | SSL, semi-supervised machine learning, transductive learning, label-efficient learning | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | 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. | Few-shot learning is a machine learning paradigm that trains models to recognize new classes or solve new tasks from only a handful of labeled examples — typically one to five — by leveraging prior knowledge acquired from a large, related training distribution. It is especially relevant in domains where labeling is expensive, scarce, or structurally limited. |
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