Structural poisson mixtures for classification of documents

Considering the statistical text classification problem we approximate class-conditional probability distributions by structurally modified Poisson mixtures. By introducing the structural model we can use different subsets of input variables to evaluate conditional probabilities of different classes in the Bayes formula. The method is applicable to document vectors of arbitrary dimension without any preprocessing. The structural optimization can be included into the EM algorithm in a statistically correct way.