Maximal Margin Labeling for Multi-Topic Text Categorization

In this paper, we address the problem of statistical learning for multitopic textcategorization (MTC), whose goal is to choose all relevant topics (a label) from a given set of topics. The proposed algorithm, Maximal MarginLabeling (MML), treats all possible labels as independent classes and learns a multi-class classifier on the induced multi-class categorization problem.To cope with the data sparseness caused by the huge number of possible labels, MML combines some prior knowledge about label prototypes and a maximal margin criterion in a novel way. Experiments withmulti-topic Web pages show that MML outperforms existing learning algorithms including Support Vector Machines.