Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Tiešsaistes pastiprināšana (Online Boosting)× | Tiešsaistes nejaušais mežs× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2001 | 2009 |
| Autors≠ | Oza, N. C. & Russell, S. | Saffari, A. et al. |
| Tips≠ | Online ensemble (incremental boosting) | Incremental ensemble (streaming decision trees) |
| Pirmavots≠ | Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann. link ↗ | 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 ↗ |
| Citi nosaukumi | streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting | ORF, streaming random forest, incremental random forest, adaptive random forest |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | 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 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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