Parametric Mixture Models for Multi-Labeled Text

  title={Parametric Mixture Models for Multi-Labeled Text},
  author={Naonori Ueda and Kazumi Saito},
We propose probabilistic generative models, called parametric mixture models (PMMs), for multiclass, multi-labeled text categorization problem. Conventionally, the binary classification approach has been employed, in which whether or not text belongs to a category is judged by the binary classifier for every category. In contrast, our approach can simultaneously detect multiple categories of text using PMMs. We derive efficient learning and prediction algorithms for PMMs. We also empirically… CONTINUE READING
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