  Transductive SVM (TSVM) is a well known semi-supervised large margin learning
method for binary text classification. In this paper we extend this method to
multi-class and hierarchical classification problems. We point out that the
determination of labels of unlabeled examples with fixed classifier weights is
a linear programming problem. We devise an efficient technique for solving it.
The method is applicable to general loss functions. We demonstrate the value of
the new method using large margin loss on a number of multi-class and
hierarchical classification datasets. For maxent loss we show empirically that
our method is better than expectation regularization/constraint and posterior
regularization methods, and competitive with the version of entropy
regularization method which uses label constraints.
