Machine learningMachine learning
半监督 Bagging
半监督 Bagging 将经典的 Bagging 集成方法扩展到标记训练样本稀缺但存在大量无标记数据的情境。在标记数据上训练的基础学习器会为无标记样本分配伪标签；然后使用扩展的数据集来构建一个多样化的集成模型，其聚合投票比单独使用有限标记集训练的任何单一模型都更准确、更稳定。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- Bennett, K. P., & Demiriz, A. (1999). Semi-supervised support vector machines. Advances in Neural Information Processing Systems, 11. MIT Press. link ↗
- Li, M., & Zhou, Z.-H. (2005). SETRED: Self-training with editing. In Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), LNAI 3518, pp. 611–621. Springer. DOI: 10.1007/11430919_71 ↗
如何引用本页
ScholarGate. (2026, June 3). Semi-supervised Bagging (Bootstrap Aggregating with Unlabeled Data). ScholarGate. https://scholargate.app/zh/machine-learning/semi-supervised-bagging
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.
Compare side by side →