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| 그래디언트 부스팅× | 온라인 배깅× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도 | 2001 | 2001 |
| 창시자≠ | Friedman, J. H. | Oza, N. C. & Russell, S. |
| 유형≠ | Ensemble (sequential boosting of decision trees) | Online ensemble (streaming bagging) |
| 원전≠ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Oza, N. C., & Russell, S. (2001). Online bagging and boosting. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001), pp. 105–112. link ↗ |
| 별칭 | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | incremental bagging, streaming bagging, online bootstrap aggregating, OzaBag |
| 관련≠ | 5 | 4 |
| 요약≠ | 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. | Online Bagging is a streaming ensemble method introduced by Oza and Russell in 2001 that adapts the classical bootstrap aggregating (Bagging) framework to the online learning setting. Instead of resampling a fixed dataset, each incoming instance is fed to every base learner a Poisson(1)-distributed number of times, faithfully approximating bootstrap sampling as the stream evolves. The result is a robust, incrementally updated ensemble that can handle concept drift and continuous data arrival without storing the entire dataset. |
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