Presentation 2013-01-24
Text Classification Using Context-Tree Weighting Algorithm for Semi-Supervised Leaning
Tomohiro OBATA, Manabu KOBAYASHI, Yoshihiko SAKASHITA,
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Abstract(in English) The Text Classification problem has been investigated by vanous techniques, such as a vector space model, a support vector machine and so on. On the other hand, Context-Tree Weighting(CTW) algonthm that has been proposed by F.M.J.Willems shows a very good compression performance. Automatic classification method applied to this CTW has been proposed, and it shows very good performance, e.g. DNA analysis. In this paper, we consider the semi-supervised leamng of the document classification for the case where the number of the learning data is not sufficient. Then we propose the semi-supervised learning methods using CTW algonthm. Moreover, the expenmental results using a newspaper data set are shown, and we show the efficiency of proposed methods.
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Keyword(in English) Text classification / Context-Tree Weighting algorithm / Data compression / Semi-Supervised Leaning
Paper # NLP2012-112,NC2012-102
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Conference Information
Committee NLP
Conference Date 2013/1/17(1days)
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Registration To Nonlinear Problems (NLP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Text Classification Using Context-Tree Weighting Algorithm for Semi-Supervised Leaning
Sub Title (in English)
Keyword(1) Text classification
Keyword(2) Context-Tree Weighting algorithm
Keyword(3) Data compression
Keyword(4) Semi-Supervised Leaning
1st Author's Name Tomohiro OBATA
1st Author's Affiliation SHONAN INSTITUTE OF TECHNOLOGY()
2nd Author's Name Manabu KOBAYASHI
2nd Author's Affiliation SHONAN INSTITUTE OF TECHNOLOGY
3rd Author's Name Yoshihiko SAKASHITA
3rd Author's Affiliation SHONAN INSTITUTE OF TECHNOLOGY
Date 2013-01-24
Paper # NLP2012-112,NC2012-102
Volume (vol) vol.112
Number (no) 389
Page pp.pp.-
#Pages 5
Date of Issue