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| Semi-overvåget Gradient Boosting× | Gradient Boosting× | |
|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2006–2010s | 2001 |
| Ophavsperson≠ | Chapelle, Scholkopf & Zien (eds.); applied to GBM variants in subsequent literature | Friedman, J. H. |
| Type≠ | Semi-supervised ensemble (self-training + gradient boosted trees) | Ensemble (sequential boosting of decision trees) |
| Oprindelig kilde≠ | Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. Proceedings of ACL 1995, 189–196. (Foundational self-training framework underlying pseudo-label approaches.) link ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Aliasser | pseudo-label gradient boosting, self-training GBM, semi-supervised GBT, label-propagation boosting | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Relaterede≠ | 6 | 5 |
| Resumé≠ | Semi-supervised gradient boosting combines gradient boosted trees with self-training or pseudo-labeling to exploit large pools of unlabeled data alongside a small labeled set. An initial GBM fit on labeled data assigns confident predictions to unlabeled examples; those pseudo-labeled points are folded back into training and the model is re-boosted, iterating until convergence. This allows practitioners to harness cheap unlabeled data when labels are scarce or expensive. | 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. |
| ScholarGateDatasæt ↗ |
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