Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Semi-supervised XGBoost× | Etikettpropagering× | |
|---|---|---|
| Ämnesområde | Maskininlärning | Maskininlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 2016–2018 | 2002 |
| Upphovsperson≠ | Chen, T. & Guestrin, C. (XGBoost); semi-supervised extension by multiple authors | Zhu, X. & Ghahramani, Z. |
| Typ≠ | Ensemble (semi-supervised gradient boosting) | Graph-based semi-supervised classification |
| Ursprungskälla≠ | 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 ↗ | Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ |
| Alias | SS-XGBoost, semi-supervised gradient boosting, pseudo-label XGBoost, label-propagation XGBoost | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation |
| Närliggande≠ | 4 | 3 |
| Sammanfattning≠ | 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. | 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. |
| ScholarGateDatamängd ↗ |
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