document: Single-shot detection of multiple categories of text using parametric mixture models In this paper , we address the problem of detecting multiple topics or categories of text where each text is not assumed to belong to one of a number of mutually exclusive categories . 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 this paper , we propose a more sophisticated approach to simultaneously detect multiple categories of text using parametric mixture models ( PMMs ) , newly presented in this paper . PMMs are probabilistic generative models for text that has multiple categories . Our PMMs are essentially different from the conventional mixture of multinomial distributions in the sense that in the former several basis multinomial parameters are mixed in the parameter space , while in the latter several multinomial components are mixed . We derive efficient learning algorithms for PMMs within the framework of the maximum a posteriori estimate . We also empirically show that our method can outperform the conventional binary approach when applied to multitopic detection of World Wide Web pages , focusing on those from the `` yahoo.com '' domain .
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