ScholarGate
助手
Machine learningMachine learning

半监督支持向量机

半监督支持向量机（S3VM）通过结合大量无标签数据和少量有标签训练集来扩展经典的SVM。它寻求一个最大间隔超平面，该超平面不仅分离有标签样本，而且穿过完整数据分布的低密度区域，从而在有标签样本稀缺时获得更好的泛化能力。

在 MethodMind 中打开即将推出视频即将推出Download slides

阅读完整方法

仅限会员

使用免费账户登录即可阅读本节。

登录

Method map

The neighbourhood of related methods — select a node to explore.

半监督支持向量机
标签传播逻辑回归随机森林支持向量机（分类）半监督高斯过程半监督K近邻半监督朴素贝叶斯

来源

  1. Joachims, T. (1999). Transductive Inference for Text Classification using Support Vector Machines. Proceedings of the 16th International Conference on Machine Learning (ICML), 200–209. link ↗
  2. 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 Support Vector Machine (S3VM / Transductive SVM). ScholarGate. https://scholargate.app/zh/machine-learning/semi-supervised-support-vector-machine

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 →

被引用于

半监督高斯过程半监督K近邻半监督朴素贝叶斯

发现本页有问题？报告或提出修改建议 →

ScholarGate — Semi-supervised Support Vector Machine (Semi-supervised Support Vector Machine (S3VM / Transductive SVM)). 于 2026-06-15 检索自 https://scholargate.app/zh/machine-learning/semi-supervised-support-vector-machine · 数据集： https://doi.org/10.5281/zenodo.20539026