An error probability estimation of the document classification using Markov model

The document classification problem has been investigated by various techniques, such as a vector space model, a support vector machine, a random forest, and so on. On the other hand, J. Ziv et al. have proposed a document classification method using Ziv-Lempel algorithm to compress the data. Furthermore, the Context-Tree Weighting (CTW) algorithm has been proposed as an outstanding data compression, and for the document classification using the CTW algorithm experimental results have been reported. In this paper, we assume that each document with same category arises from Markov model with same parameters for the document classification. Then we propose an analysis method to estimate a classification error probability for the document with the finite length.