document: Unsupervised transfer classification : application to text categorization We study the problem of building the classification model for a target class in the absence of any labeled training example for that class . To address this difficult learning problem , we extend the idea of transfer learning by assuming that the following side information is available : ( i ) a collection of labeled examples belonging to other classes in the problem domain , called the auxiliary classes ; ( ii ) the class information including the prior of the target class and the correlation between the target class and the auxiliary classes . Our goal is to construct the classification model for the target class by leveraging the above data and information . We refer to this learning problem as unsupervised transfer classification . Our framework is based on the generalized maximum entropy model that is effective in transferring the label information of the auxiliary classes to the target class . A theoretical analysis shows that under certain assumption , the classification model obtained by the proposed approach converges to the optimal model when it is learned from the labeled examples for the target class . Empirical study on text categorization over four different data sets verifies the effectiveness of the proposed approach .
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