Reading Notes-Chapter 13 of "Machine Learning": Semi-supervised Learning

• Purpose: Use the information contained in unlabeled samples to improve the generalization ability of the model
• Active learning: Effectively interact with the outside world to reduce marking costs. Use as few "queries" as possible to get the best possible performance
• Semi-supervised learning: The learner does not rely on external interaction and automatically uses unlabeled samples to improve learning performance
  • Pure semi-supervised learning
  • Direct learning
• Basic assumption: similar samples have similar outputs

• • Clustering hypothesis
  • Manifold hypothesis

• Four paradigms of semi-supervised learning (classification)
  • Generative method
  • Semi-supervised SVM
  • Figure semi-supervised learning
  • Divergence-based approach
• Semi-supervised clustering