Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Boosting× | Robust Bagging× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 1990–1997 | 1996–2000s |
| Autor original≠ | Schapire, R. E.; Freund, Y. | Breiman, L. (bagging); robust variants developed by various authors in 2000s |
| Tipo≠ | Sequential ensemble (iterative reweighting) | Ensemble (robust bootstrap aggregating) |
| Fonte seminal≠ | 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 ↗ | Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. DOI ↗ |
| Outros nomes | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing |
| Relacionados | 6 | 6 |
| Resumo≠ | 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. | Robust Bagging extends the classic Bootstrap Aggregating (Bagging) framework by replacing or augmenting standard base learners with robust estimators — or by using robust aggregation rules — so that the ensemble remains accurate even when training data contain outliers, mislabelled instances, or heavy-tailed noise distributions. |
| ScholarGateConjunto de dados ↗ |
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