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| Gradient-Boosting mit aktivem Lernen× | Gradient Boosting× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2000s–2010s | 2001 |
| Urheber≠ | Settles, B. (active learning); Friedman, J. H. (gradient boosting); combined framework developed by the research community | Friedman, J. H. |
| Typ≠ | Active learning framework with gradient boosting base learner | Ensemble (sequential boosting of decision trees) |
| Wegweisende Quelle≠ | Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison. link ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Aliasnamen | AL-GBM, gradient boosting active learner, active gradient boosting, active learning with boosted trees | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Verwandt≠ | 4 | 5 |
| Zusammenfassung≠ | Active Learning Gradient Boosting combines the powerful predictive accuracy of gradient boosted trees with an active learning loop that selects the most informative unlabeled examples for human annotation. By querying only the instances the model is most uncertain about, the method achieves high accuracy with far fewer labeled examples than passive supervised learning. | 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. |
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