Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Online Boosting× | Gradient Boosting× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem | 2001 | 2001 |
| Autor original≠ | Oza, N. C. & Russell, S. | Friedman, J. H. |
| Tipo≠ | Online ensemble (incremental boosting) | Ensemble (sequential boosting of decision trees) |
| Fonte seminal≠ | Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann. link ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Outros nomes | streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Relacionados≠ | 6 | 5 |
| Resumo≠ | 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. | 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. |
| ScholarGateConjunto de dados ↗ |
|
|