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正则化半监督学习

正则化半监督学习向半监督目标函数中显式添加基于几何或图的惩罚项，使得决策函数在数据流形上平滑变化。该方法以流形正则化（Belkin, Niyogi & Sindhwani, 2006）为先驱，利用标记和未标记样本的结构，在标记数据稀缺时学习比单独监督正则化更准确的模型。

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正则化半监督学习
高斯过程标签传播正则化逻辑回归正则化随机森林自监督学习半监督学习

来源

  1. Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7, 2399–2434. 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). Regularized Semi-Supervised Learning (Manifold Regularization and Graph-Based SSL). ScholarGate. https://scholargate.app/zh/machine-learning/regularized-semi-supervised-learning

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ScholarGate — Regularized semi-supervised learning (Regularized Semi-Supervised Learning (Manifold Regularization and Graph-Based SSL)). 于 2026-06-15 检索自 https://scholargate.app/zh/machine-learning/regularized-semi-supervised-learning · 数据集： https://doi.org/10.5281/zenodo.20539026