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| 준지도 전이 학습× | 준지도 학습× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2010s | 1970s–2006 (formalized) |
| 창시자≠ | Pan, S. J. & Yang, Q. (formalized); wider community | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| 유형≠ | Hybrid learning paradigm | Learning paradigm |
| 원전≠ | 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 ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| 별칭 | SSTL, semi-supervised domain adaptation, transfer learning with unlabeled data, few-label transfer learning | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| 관련≠ | 4 | 5 |
| 요약≠ | 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. | Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained. |
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