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| 준지도 학습 배깅× | 준지도 학습× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2000s | 1970s–2006 (formalized) |
| 창시자≠ | Various (Breiman bagging + semi-supervised extensions, 1990s–2000s) | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| 유형≠ | Semi-supervised ensemble (bagging variant) | Learning paradigm |
| 원전≠ | Bennett, K. P., & Demiriz, A. (1999). Semi-supervised support vector machines. Advances in Neural Information Processing Systems, 11. MIT Press. link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| 별칭 | SS-Bagging, semi-supervised bootstrap aggregating, self-training bagging, bagging with pseudo-labels | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| 관련≠ | 4 | 5 |
| 요약≠ | Semi-supervised Bagging extends the classical bagging ensemble to settings where labeled training examples are scarce but large amounts of unlabeled data are available. Base learners trained on labeled data assign pseudo-labels to unlabeled examples; the expanded dataset is then used to grow a diverse ensemble whose aggregated vote is more accurate and more stable than any single model trained on the limited labeled set alone. | 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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