published: July 30, 2009, recorded: June 2009, views: 22880
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !