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| 준지도 학습 부스팅× | 그래디언트 부스팅× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1999–2009 | 2001 |
| 창시자≠ | Mallapragada, P. K.; Bennett, K. P.; and others | Friedman, J. H. |
| 유형≠ | Semi-supervised ensemble method | Ensemble (sequential boosting of decision trees) |
| 원전≠ | 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 ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| 별칭 | SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| 관련 | 5 | 5 |
| 요약≠ | 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. | Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost. |
| ScholarGate데이터셋 ↗ |
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