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| Samouczenie gradientowe× | XGBoost× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2020s | 2016 |
| Twórca≠ | Various researchers (Zhang et al. and others) | Chen, T. & Guestrin, C. |
| Typ≠ | Ensemble (self-supervised + gradient boosting) | Ensemble (gradient-boosted decision trees) |
| Źródło pierwotne≠ | Zhang, Y., Zhang, J., & Yang, Q. (2022). Self-Supervised Gradient Boosting for Semi-Supervised Learning on Tabular Data. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. link ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Inne nazwy≠ | SSL gradient boosting, self-supervised boosting, semi-supervised gradient boosting, SSL-GBM | XGBoost, extreme gradient boosting, scalable tree boosting |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | Self-supervised gradient boosting extends the classic gradient boosting framework by incorporating self-supervised pretext tasks to exploit unlabeled data. The model first learns useful feature representations from unannotated samples, then uses those representations to guide the sequential ensemble of weak learners, achieving strong predictive performance even when labeled examples are scarce. | XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions. |
| ScholarGateZbiór danych ↗ |
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