Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Regularizētā pārvietotā mācīšanās× | Daļēji uzraudzīta pārsūtīšanas mācīšanās× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2000s–2010s | 2010s |
| Autors≠ | Pan, S. J. & Yang, Q. (survey); regularization variants by multiple authors | Pan, S. J. & Yang, Q. (formalized); wider community |
| Tips≠ | Regularized supervised/semi-supervised learning framework | Hybrid learning paradigm |
| Pirmavots≠ | Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ | Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021). A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1), 43–76. DOI ↗ |
| Citi nosaukumi | regularized domain adaptation, transfer learning with regularization, penalized transfer learning, regularized fine-tuning | SSTL, semi-supervised domain adaptation, transfer learning with unlabeled data, few-label transfer learning |
| Saistītās≠ | 6 | 4 |
| Kopsavilkums≠ | Regularized Transfer Learning applies explicit penalty terms to a transfer learning pipeline to control how much a model shifts away from source-domain knowledge when adapting to a new target domain. The regularizer discourages negative transfer — the harmful carry-over of irrelevant source patterns — while preserving beneficial shared representations and preventing overfitting when target-domain labels are scarce. | Semi-supervised Transfer Learning combines knowledge transferred from a richly labeled source domain with the structure of abundant unlabeled target-domain data, using only a small set of labeled target examples to achieve strong generalization where full annotation is scarce or expensive. |
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