Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Online Boosting× | Boosting× | |
|---|---|---|
| Domeniu | Învățare automată | Învățare automată |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2001 | 1990–1997 |
| Autorul original≠ | Oza, N. C. & Russell, S. | Schapire, R. E.; Freund, Y. |
| Tip≠ | Online ensemble (incremental boosting) | Sequential ensemble (iterative reweighting) |
| Sursa seminală≠ | Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann. link ↗ | 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 ↗ |
| Denumiri alternative | streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble |
| Înrudite | 6 | 6 |
| Rezumat≠ | 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. | 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. |
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