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| 온라인 랜덤 포레스트× | 온라인 그래디언트 부스팅× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2009 | 2011–2015 |
| 창시자≠ | Saffari, A. et al. | Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al. |
| 유형≠ | Incremental ensemble (streaming decision trees) | Online ensemble (sequential boosting on streaming data) |
| 원전≠ | Saffari, A., Leistner, C., Santner, J., Godec, M., & Bischof, H. (2009). On-line random forests. In Proceedings of the 3rd IEEE International Workshop on On-Line Learning for Computer Vision (OLCV 2009), pp. 1–8. IEEE. link ↗ | Grubb, A. & Bagnell, J. A. (2011). Generalized Boosting Algorithms for Convex Optimization. Proceedings of the 28th International Conference on Machine Learning (ICML 2011), 1209–1216. link ↗ |
| 별칭 | ORF, streaming random forest, incremental random forest, adaptive random forest | OGB, streaming gradient boosting, incremental gradient boosting, online boosting with gradient descent |
| 관련 | 6 | 6 |
| 요약≠ | Online Random Forest (ORF) extends the classic Random Forest to streaming settings, updating each tree incrementally as new observations arrive without storing or replaying the full training set. Algorithms such as Adaptive Random Forests (ARF) add drift detection so the ensemble adapts when the data distribution changes over time. | Online Gradient Boosting adapts the gradient boosting framework for streaming settings where data arrives one sample at a time rather than as a fixed batch. At each step the model computes a pseudo-residual for the incoming observation and updates a weak learner in place, growing an additive ensemble without storing or revisiting past data. This makes it suitable for real-time prediction and large-scale streaming pipelines where retraining from scratch is infeasible. |
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