Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uimarishaji Imara× | Msitu Imara wa Misitu× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Mashine | Ujifunzaji wa Mashine |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 1999–2001 | 2000s–2010s |
| Mwanzilishi≠ | Freund, Y.; Mason, L. et al. | Various (extensions of Breiman 2001 Random Forest) |
| Aina≠ | Ensemble (robust sequential boosting) | Robust Ensemble (noise-tolerant bagging of decision trees) |
| Chanzo asilia≠ | Freund, Y. (2001). An adaptive version of the boost by majority algorithm. Machine Learning, 43(3), 293–318. DOI ↗ | Chen, S., & Guestrin, C. (2019). Robust Random Forest. In Proceedings of the 36th International Conference on Machine Learning (ICML). Also see: Gao, W., & Zhou, Z.-H. (2013). On the Doubt about Margin Explanation of Boosting. Artificial Intelligence, 203, 1–18. link ↗ |
| Majina mbadala | noise-tolerant boosting, robust AdaBoost, boosting with robust losses, outlier-resistant boosting | RRF, noise-robust random forest, outlier-resistant random forest, robust ensemble forest |
| Zinazohusiana | 6 | 6 |
| Muhtasari≠ | 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. | Robust Random Forest extends the standard Random Forest ensemble by incorporating mechanisms that reduce the influence of outliers, label noise, and corrupted observations. Rather than treating all training instances equally, it applies weighting or filtering strategies so that noisy or anomalous samples contribute less to individual tree splits, yielding predictions that remain reliable even when data quality is imperfect. |
| ScholarGateSeti ya data ↗ |
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