Methoden vergleichen
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| Boosting× | Online Random Forest× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1990–1997 | 2009 |
| Urheber≠ | Schapire, R. E.; Freund, Y. | Saffari, A. et al. |
| Typ≠ | Sequential ensemble (iterative reweighting) | Incremental ensemble (streaming decision trees) |
| Wegweisende Quelle≠ | Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. DOI ↗ | 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 ↗ |
| Aliasnamen | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | ORF, streaming random forest, incremental random forest, adaptive random forest |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy. | 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. |
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