### 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 language | English |
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Title of host publication | 2012 International Symposium on Information Theory and Its Applications, ISITA 2012 |

Pages | 717-721 |

Number of pages | 5 |

Publication status | Published - 2012 |

Event | 2012 International Symposium on Information Theory and Its Applications, ISITA 2012 - Honolulu, HI Duration: 2012 Oct 28 → 2012 Oct 31 |

### Other

Other | 2012 International Symposium on Information Theory and Its Applications, ISITA 2012 |
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City | Honolulu, HI |

Period | 12/10/28 → 12/10/31 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Science Applications
- Information Systems

### Cite this

*2012 International Symposium on Information Theory and Its Applications, ISITA 2012*(pp. 717-721). [6401034]

**An error probability estimation of the document classification using Markov model.** / Kobayashi, Manabu; Ninomiya, Hiroshi; Matsushima, Toshiyasu; Hirasawa, Shigeichi.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*2012 International Symposium on Information Theory and Its Applications, ISITA 2012.*, 6401034, pp. 717-721, 2012 International Symposium on Information Theory and Its Applications, ISITA 2012, Honolulu, HI, 12/10/28.

}

TY - GEN

T1 - An error probability estimation of the document classification using Markov model

AU - Kobayashi, Manabu

AU - Ninomiya, Hiroshi

AU - Matsushima, Toshiyasu

AU - Hirasawa, Shigeichi

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84873556641&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84873556641&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84873556641

SN - 9784885522673

SP - 717

EP - 721

BT - 2012 International Symposium on Information Theory and Its Applications, ISITA 2012

ER -