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| 온라인 부스팅× | 온라인 배깅× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도 | 2001 | 2001 |
| 창시자 | Oza, N. C. & Russell, S. | Oza, N. C. & Russell, S. |
| 유형≠ | Online ensemble (incremental boosting) | Online ensemble (streaming bagging) |
| 원전≠ | Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann. link ↗ | 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 | streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting | incremental bagging, streaming bagging, online bootstrap aggregating, OzaBag |
| 관련≠ | 6 | 4 |
| 요약≠ | Online Boosting adapts the classical boosting framework to data streams, updating an ensemble of weak learners one example at a time without storing the full dataset. The Oza-Russell formulation approximates AdaBoost's reweighting using Poisson-sampled instance counts, enabling accurate, adaptive classification in real-time or resource-constrained environments. | 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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