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| 준지도 학습 투표 앙상블× | 부스팅× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1998–2005 | 1990–1997 |
| 창시자≠ | Zhou, Z.-H. & Li, M. (tri-training); Blum & Mitchell (co-training) | Schapire, R. E.; Freund, Y. |
| 유형≠ | Semi-supervised ensemble (voting) | Sequential ensemble (iterative reweighting) |
| 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 ↗ | Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. DOI ↗ |
| 별칭 | semi-supervised majority vote, SSL voting ensemble, co-training voting classifier, semi-supervised multi-classifier voting | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble |
| 관련≠ | 5 | 6 |
| 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. | Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy. |
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