We propose probabilistic generative models, called parametric mix- ture models (PMMs), for multiclass, multi-labeled text categoriza- tion problem. Conventionally, the binary classi(cid:12)cation approach has been employed, in which whether or not text belongs to a cat- egory is judged by the binary classi(cid:12)er for every category. In con- trast, our approach can simultaneously detect multiple categories of text using PMMs. We also empirically show that our method could signi(cid:12)cantly outperform the conventional binary methods when ap- plied to multi-labeled text categorization using real World Wide Web pages.

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 show that our method could significantly outperform the conventional binary methods when applied to multi-labeled text categorization using real World Wide Web pages.

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 show that our method could significantly outperform the conventional binary methods when applied to multi-labeled text categorization using real World Wide Web pages.

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 isjudged 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 forPMMs. We also empirically show that our method could significantly outperform the conventional binary methods when applied tomulti-labeled text categorization using real World Wide Web pages.