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| Pooljärelevalvega meetrika õppimine× | Väheste näidistega õppimine× | |
|---|---|---|
| Valdkond | Masinõpe | Masinõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2007–2008 | 2011–2017 |
| Looja≠ | Yeung, D.-Y. & Chang, H.; Davis, J. V. & Dhillon, I. S. | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| Tüüp≠ | Hybrid supervised/unsupervised distance learning | Meta-learning / low-data learning paradigm |
| Algallikas≠ | Yeung, D.-Y., & Chang, H. (2007). A kernel approach for semi-supervised metric learning. IEEE Transactions on Neural Networks, 18(1), 141–149. DOI ↗ | 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 ↗ |
| Rööpnimetused | SSML, semi-supervised distance learning, constrained metric learning, weakly supervised metric learning | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| Seotud≠ | 5 | 4 |
| Kokkuvõte≠ | Semi-supervised metric learning learns a task-adapted distance function by combining a small set of labeled pairwise constraints — must-link and cannot-link pairs — with the geometric structure of a much larger pool of unlabeled data. The result is a Mahalanobis-style or kernel-based distance that reflects both supervision and data topology, improving downstream tasks such as nearest-neighbor classification and clustering. | 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. |
| ScholarGateAndmestik ↗ |
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