Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Robust Boosting× | Gradient Boosting× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 1999–2001 | 2001 |
| Kehittäjä≠ | Freund, Y.; Mason, L. et al. | Friedman, J. H. |
| Tyyppi≠ | Ensemble (robust sequential boosting) | Ensemble (sequential boosting of decision trees) |
| Alkuperäislähde≠ | Freund, Y. (2001). An adaptive version of the boost by majority algorithm. Machine Learning, 43(3), 293–318. DOI ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Rinnakkaisnimet | noise-tolerant boosting, robust AdaBoost, boosting with robust losses, outlier-resistant boosting | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Liittyvät≠ | 6 | 5 |
| Tiivistelmä≠ | 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. | 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. |
| ScholarGateAineisto ↗ |
|
|