Parametric Mixture Models for Multi-Labeled Text

@inproceedings{Ueda2002ParametricMM,
  title={Parametric Mixture Models for Multi-Labeled Text},
  author={Naonori Ueda and Kazumi Saito},
  booktitle={NIPS},
  year={2002}
}
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
Highly Influential
This paper has highly influenced 30 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 359 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 199 extracted citations

359 Citations

02040'05'08'11'14'17
Citations per Year
Semantic Scholar estimates that this publication has 359 citations based on the available data.

See our FAQ for additional information.

Similar Papers

Loading similar papers…