Semi-Supervised Learning

author: Jerry (Xiaojin) Zhu, Department of Computer Sciences, University of Wisconsin-Madison
published: July 30, 2009,   recorded: June 2009,   views: 22881


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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.

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Comment1 Naeem Iqbal, September 13, 2009 at 2:15 a.m.:

hi how are you i have a problem in downloading this URL: can you help me out. waiting for reply

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