Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Kuimarisha× | Uimarishaji wenye Nguvu wa Kukuza (Robust Gradient Boosting)× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Mashine | Ujifunzaji wa Mashine |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 1990–1997 | 2001 |
| Mwanzilishi≠ | Schapire, R. E.; Freund, Y. | Friedman, J. H. (with Huber loss from Huber, P. J.) |
| Aina≠ | Sequential ensemble (iterative reweighting) | Ensemble (boosted trees with robust loss) |
| Chanzo asilia≠ | 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 ↗ | Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Majina mbadala | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees |
| Zinazohusiana | 6 | 6 |
| Muhtasari≠ | 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 Gradient Boosting is gradient boosting trained with outlier-resistant loss functions — most commonly the Huber loss or quantile (pinball) loss — instead of squared-error loss. Proposed in Friedman's seminal 2001 paper, this variant produces predictions far less distorted by extreme values or contaminated labels, while retaining the full predictive power of gradient-boosted trees. |
| ScholarGateSeti ya data ↗ |
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