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| Bagging (agregacja bootstrapowa)× | Solidne zwiększanie (Robust Boosting)× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1996 | 1999–2001 |
| Twórca≠ | Breiman, L. | Freund, Y.; Mason, L. et al. |
| Typ≠ | Ensemble meta-algorithm (variance reduction via bootstrap aggregation) | Ensemble (robust sequential boosting) |
| Źródło pierwotne≠ | Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140. DOI ↗ | Freund, Y. (2001). An adaptive version of the boost by majority algorithm. Machine Learning, 43(3), 293–318. DOI ↗ |
| Inne nazwy≠ | Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor | noise-tolerant boosting, robust AdaBoost, boosting with robust losses, outlier-resistant boosting |
| Pokrewne≠ | 5 | 6 |
| Podsumowanie≠ | Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner. | Robust Boosting modifies standard boosting algorithms — such as AdaBoost or gradient boosting — by replacing the default exponential or squared loss with robust loss functions (e.g., Huber, logistic, or truncated losses) or by incorporating noise-tolerance mechanisms, so that the ensemble remains accurate even when training data contain outliers, label noise, or heavy-tailed errors. |
| ScholarGateZbiór danych ↗ |
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