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| Градијентно појачање× | Propagacija oznaka× | |
|---|---|---|
| Oblast | Mašinsko učenje | Mašinsko učenje |
| Porodica | Machine learning | Machine learning |
| Godina nastanka≠ | 2001 | 2002 |
| Tvorac≠ | Friedman, J. H. | Zhu, X. & Ghahramani, Z. |
| Tip≠ | Ensemble (sequential boosting of decision trees) | Graph-based semi-supervised classification |
| Temeljni izvor≠ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ |
| Drugi nazivi | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation |
| Srodne≠ | 5 | 3 |
| Sažetak≠ | 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. | Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data. |
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