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| 앙상블 능동 학습× | 부스팅× | 준지도 학습× | |
|---|---|---|---|
| 분야 | 머신러닝 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning | Machine learning |
| 기원 연도≠ | 1992 | 1990–1997 | 1970s–2006 (formalized) |
| 창시자≠ | Seung, H. S., Opper, M., & Sompolinsky, H. | Schapire, R. E.; Freund, Y. | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| 유형≠ | Ensemble-based active learning strategy | Sequential ensemble (iterative reweighting) | Learning paradigm |
| 원전≠ | Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory (COLT 1992), pp. 287–294. ACM. link ↗ | 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 ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| 별칭 | Query by Committee, QBC active learning, committee-based active learning, ensemble query strategy | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| 관련≠ | 5 | 6 | 5 |
| 요약≠ | Ensemble Active Learning combines a committee of diverse models with an active learning loop to select the most informative unlabeled examples for labeling. Rooted in the Query by Committee framework introduced by Seung et al. (1992), it uses disagreement among committee members as a signal for uncertainty, reducing the number of labeled examples needed to achieve strong predictive performance. | 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. | 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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