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| 준지도 학습 투표 앙상블× | 준지도 학습 배깅× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1998–2005 | 2000s |
| 창시자≠ | Zhou, Z.-H. & Li, M. (tri-training); Blum & Mitchell (co-training) | Various (Breiman bagging + semi-supervised extensions, 1990s–2000s) |
| 유형≠ | Semi-supervised ensemble (voting) | Semi-supervised ensemble (bagging variant) |
| 원전≠ | Zhou, Z.-H., & Li, M. (2005). Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 17(11), 1529–1541. DOI ↗ | Bennett, K. P., & Demiriz, A. (1999). Semi-supervised support vector machines. Advances in Neural Information Processing Systems, 11. MIT Press. link ↗ |
| 별칭 | semi-supervised majority vote, SSL voting ensemble, co-training voting classifier, semi-supervised multi-classifier voting | SS-Bagging, semi-supervised bootstrap aggregating, self-training bagging, bagging with pseudo-labels |
| 관련≠ | 5 | 4 |
| 요약≠ | A semi-supervised voting ensemble trains multiple classifiers on a small labeled set, then iteratively exploits unlabeled data by having the classifiers label examples they agree on, expanding the training pool until all classifiers vote jointly on test examples. It combines the label-efficiency of semi-supervised learning with the variance-reduction of majority-vote ensembles, making it valuable when annotation is costly. | 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. |
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