An error probability estimation of the document classification using Markov model

Manabu Kobayashi, Hiroshi Ninomiya, Toshiyasu Matsushima, Shigeichi Hirasawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2012 International Symposium on Information Theory and Its Applications, ISITA 2012
Pages717-721
Number of pages5
Publication statusPublished - 2012 Dec 1
Event2012 International Symposium on Information Theory and Its Applications, ISITA 2012 - Honolulu, HI, United States
Duration: 2012 Oct 282012 Oct 31

Publication series

Name2012 International Symposium on Information Theory and Its Applications, ISITA 2012

Conference

Conference2012 International Symposium on Information Theory and Its Applications, ISITA 2012
CountryUnited States
CityHonolulu, HI
Period12/10/2812/10/31

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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    Kobayashi, M., Ninomiya, H., Matsushima, T., & Hirasawa, S. (2012). An error probability estimation of the document classification using Markov model. In 2012 International Symposium on Information Theory and Its Applications, ISITA 2012 (pp. 717-721). [6401034] (2012 International Symposium on Information Theory and Its Applications, ISITA 2012).