Material Detail

Semi-Supervised Learning

Semi-Supervised Learning

This video was recorded at Machine Learning Summer School (MLSS), Chicago 2009. This tutorial covers classification approaches that utilize both labeled and unlabeled data. We will review self-training, Gaussian mixture models, co-training, multiview learning, graph-transduction and manifold regularization, transductive SVMs, and a PAC bound for semi-supervised learning. We then discuss some new development, including online semi-supervised learning, multi-manifold learning, and human semi-supervised learning.
Rate

Quality

  • User Rating
  • Comments
  • Learning Exercises
  • Bookmark Collections
  • Course ePortfolios
  • Accessibility Info

More about this material

Comments

Log in to participate in the discussions or sign up if you are not already a MERLOT member.
hidden