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| Полу-надгледана случајна шума× | Градијентно појачање× | Propagacija oznaka× | |
|---|---|---|---|
| Oblast | Mašinsko učenje | Mašinsko učenje | Mašinsko učenje |
| Porodica | Machine learning | Machine learning | Machine learning |
| Godina nastanka≠ | 2009 | 2001 | 2002 |
| Tvorac≠ | Leistner, C., Saffari, A., Santner, J., & Bischof, H. | Friedman, J. H. | Zhu, X. & Ghahramani, Z. |
| Tip≠ | Semi-supervised ensemble classifier | Ensemble (sequential boosting of decision trees) | Graph-based semi-supervised classification |
| Temeljni izvor≠ | Leistner, C., Saffari, A., Santner, J., & Bischof, H. (2009). Semi-supervised random forests. In Proceedings of the IEEE 12th International Conference on Computer Vision (ICCV), pp. 506–513. IEEE. DOI ↗ | 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 | SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation |
| Srodne≠ | 3 | 5 | 3 |
| Sažetak≠ | Semi-supervised Random Forest (SSL-RF) extends the classic Random Forest by exploiting both labeled and unlabeled training examples. When labeling data is expensive or time-consuming, SSL-RF assigns tentative pseudo-labels to unlabeled observations through the forest itself, then retrains on the enriched dataset, progressively improving accuracy without requiring additional human annotation. | 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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