Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daļēji uzraudzīts XGBoost× | Gradient Boosting× | Iezīmju izplatīšana× | |
|---|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2016–2018 | 2001 | 2002 |
| Autors≠ | Chen, T. & Guestrin, C. (XGBoost); semi-supervised extension by multiple authors | Friedman, J. H. | Zhu, X. & Ghahramani, Z. |
| Tips≠ | Ensemble (semi-supervised gradient boosting) | Ensemble (sequential boosting of decision trees) | Graph-based semi-supervised classification |
| Pirmavots≠ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. 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 ↗ |
| Citi nosaukumi | SS-XGBoost, semi-supervised gradient boosting, pseudo-label XGBoost, label-propagation XGBoost | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation |
| Saistītās≠ | 4 | 5 | 3 |
| Kopsavilkums≠ | Semi-supervised XGBoost extends the XGBoost gradient boosting framework to settings where only a fraction of training examples carry labels. By iteratively generating pseudo-labels for unlabeled data and retraining on the expanded set, the method extracts signal from unlabeled observations, improving generalization when labeled data are scarce. | 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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