Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Aprendizado de Métrica Semissupervisionado× | Aprendizagem por Transferência× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 2007–2008 | 2010 (formalized); 1990s (early roots) |
| Autor original≠ | Yeung, D.-Y. & Chang, H.; Davis, J. V. & Dhillon, I. S. | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Tipo≠ | Hybrid supervised/unsupervised distance learning | Learning paradigm |
| Fonte seminal≠ | Yeung, D.-Y., & Chang, H. (2007). A kernel approach for semi-supervised metric learning. IEEE Transactions on Neural Networks, 18(1), 141–149. DOI ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| Outros nomes | SSML, semi-supervised distance learning, constrained metric learning, weakly supervised metric learning | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Relacionados≠ | 5 | 3 |
| Resumo≠ | 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. | 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. |
| ScholarGateConjunto de dados ↗ |
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