方法对比
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| 半监督决策树× | 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≠ | 2000s | 2001 |
| 提出者≠ | Various (Levin & Shapiro; Zhu & Goldberg lineage) | Friedman, J. H. |
| 类型≠ | Semi-supervised classifier / regressor | Ensemble (sequential boosting of decision trees) |
| 开创性文献≠ | Levin, E. & Shapiro, E. (2000). Learning Decision Trees from Semi-labeled Examples. Proceedings of the ICML Workshop on Attribute-Value and Relational Learning. link ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| 别名 | SSDT, semi-supervised tree induction, self-training decision tree, label-propagation tree | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| 相关≠ | 4 | 5 |
| 摘要≠ | A Semi-supervised Decision Tree extends standard decision tree induction — such as CART or C4.5 — to exploit unlabeled observations alongside the labeled training set. By iteratively assigning tentative labels to unlabeled data and incorporating them into the growing or splitting process, the algorithm can achieve better accuracy than a fully supervised tree trained on the labeled subset alone, which is especially valuable when labeling is expensive or time-consuming. | 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. |
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