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| 정규화된 전이 학습× | 메트릭 학습× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2000s–2010s | 2003 (foundational); refined 2009 (LMNN) |
| 창시자≠ | Pan, S. J. & Yang, Q. (survey); regularization variants by multiple authors | Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y. |
| 유형≠ | Regularized supervised/semi-supervised learning framework | Representation learning / supervised distance optimization |
| 원전≠ | Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ | 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 ↗ |
| 별칭 | regularized domain adaptation, transfer learning with regularization, penalized transfer learning, regularized fine-tuning | Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance |
| 관련≠ | 6 | 5 |
| 요약≠ | 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. | 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. |
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