Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Ansambļa metrikas mācīšanās× | Mācīšanās ar maziem paraugu skaitu× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2000s–2010s | 2011–2017 |
| Autors≠ | Multiple contributors (Weinberger, Saul, et al.) | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| Tips≠ | Ensemble of learned distance metrics | Meta-learning / low-data learning paradigm |
| Pirmavots≠ | Wang, J., Kalousis, A., & Woznica, A. (2012). Parametric local metric learning for nearest neighbor classification. Advances in Neural Information Processing Systems, 25. link ↗ | 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 | EML, ensemble distance metric learning, multiple metric fusion, combined metric learning | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | Ensemble Metric Learning trains multiple distance metric learners — each on a different data view, feature subspace, or with a different objective — and combines the resulting metrics to produce a single, more robust similarity function. Combining diverse metrics reduces the variance of any individual metric and improves performance in tasks such as nearest-neighbor classification, retrieval, and few-shot learning. | 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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