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| 베이즈 랜덤 포레스트× | 준지도 학습 부스팅× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2015 | 1999–2009 |
| 창시자≠ | Taddy, M. et al. | Mallapragada, P. K.; Bennett, K. P.; and others |
| 유형≠ | Bayesian ensemble of decision trees | Semi-supervised ensemble method |
| 원전≠ | Taddy, M., Chen, C., Yu, J., & Wyle, M. (2015). Bayesian and Empirical Bayesian Forests. Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), PMLR 37, 967–976. link ↗ | Mallapragada, P. K., Jin, R., Jain, A. K., & Liu, Y. (2009). SemiBoost: Boosting for Semi-supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11), 2000–2014. DOI ↗ |
| 별칭 | Bayesian Forest, BRF, Empirical Bayesian Forest, posterior random forest | SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting |
| 관련 | 5 | 5 |
| 요약≠ | Bayesian Random Forest extends the classical random forest by placing a prior distribution over tree structures and leaf parameters, then sampling or approximating the posterior over that ensemble. The result is a set of predictions accompanied by calibrated uncertainty estimates — a capability standard random forests lack — making it valuable when knowing how confident the model is matters as much as the prediction itself. | Semi-supervised Boosting is an ensemble learning paradigm that extends classical boosting algorithms — such as AdaBoost — to exploit both labeled and unlabeled data. By propagating label information through a similarity structure over unlabeled instances, it trains stronger classifiers than supervised boosting alone when labeled data are scarce. |
| ScholarGate데이터셋 ↗ |
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