A Note on Semi-Supervised Learning using Markov Random Fields

This paper describes conditional-probability training of Markov random elds using combinations of labeled and unlabeled data. We capture the similarities between instances learning the appropriate distance metric from the data. The likelihood model and several training procedures are presented.

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