方法对比
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| 自监督梯度提升 (Self-supervised Gradient Boosting)× | 梯度提升（Gradient Boosting）× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2020s | 2001 |
| 提出者≠ | Various researchers (Zhang et al. and others) | Friedman, J. H. |
| 类型≠ | Ensemble (self-supervised + gradient boosting) | Ensemble (sequential boosting of decision trees) |
| 开创性文献≠ | 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 ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| 别名 | SSL gradient boosting, self-supervised boosting, semi-supervised gradient boosting, SSL-GBM | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| 相关 | 5 | 5 |
| 摘要≠ | 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. | 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. |
| ScholarGate数据集 ↗ |
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