Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Gradient Boosting× | Tiešsaistes pastiprināšana (Online Boosting)× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads | 2001 | 2001 |
| Autors≠ | Friedman, J. H. | Oza, N. C. & Russell, S. |
| Tips≠ | Ensemble (sequential boosting of decision trees) | Online ensemble (incremental boosting) |
| Pirmavots≠ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann. link ↗ |
| Citi nosaukumi | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost. | 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. |
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