Machine learningMachine learning
半监督 CatBoost
半监督 CatBoost 将 CatBoost 的有序梯度提升框架应用于仅有部分训练实例带有标签的场景，通过伪标签或一致性策略利用无标签数据，以提高模型精度，使其超越仅凭有标签数据所能达到的水平。
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来源
- Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: unbiased boosting with categorical features. In Advances in Neural Information Processing Systems (NeurIPS), 31. link ↗
- Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
如何引用本页
ScholarGate. (2026, June 3). Semi-supervised CatBoost (Gradient Boosting with Partially Labeled Data). ScholarGate. https://scholargate.app/zh/machine-learning/semi-supervised-catboost
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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