Center for Automated Learning and Discovery
School of Computer Science, Carnegie Mellon University


Towards Semi-Supervised Classification with
Markov Random Fields

Xiaojin Zhu, Zoubin Ghahramani

June 2002


Keywords: Artificial intelligence:learning, pattern recognition: models-statistical, pattern recognition: design methodology-classifier design and evaluation, semi-supervised learning, Boltzmann machine

We investigate the use of Boltzmann machines in semi-supervised classification. We treat the labeled / unlabeled dataset as a Markov random field, and derive a Boltzmann machine learning algorithm for it to learn the feature weights, label noise and labels for unlabeled data all at once. We present some Markov chain Monte Carlo methods needed for learning, and discuss the need to regularize model parameters. Preliminary experimental results are presented.

26 pages

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