Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Učení metrik× | Přenosové učení× | |
|---|---|---|
| Obor | Strojové učení | Strojové učení |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 2003 (foundational); refined 2009 (LMNN) | 2010 (formalized); 1990s (early roots) |
| Tvůrce≠ | Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y. | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Typ≠ | Representation learning / supervised distance optimization | Learning paradigm |
| Původní zdroj≠ | Xing, E. P., Jordan, M. I., Russell, S., & Ng, A. Y. (2003). Distance metric learning with application to clustering with side-information. In Advances in Neural Information Processing Systems (NIPS), 16, 505–512. link ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| Další názvy | Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Příbuzné≠ | 5 | 3 |
| Shrnutí≠ | Metric learning is a machine-learning framework that trains a distance or similarity function from data so that semantically similar examples end up close together in the learned space while dissimilar examples are pushed apart. Unlike fixed distances such as Euclidean, the learned metric adapts to the structure of the task, making downstream classifiers, clusterers, and retrieval systems significantly more accurate. | Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond. |
| ScholarGateDatová sada ↗ |
|
|