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. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Text classification / Context-Tree Weighting algorithm / Data compression / Semi-Supervised Leaning |
Paper # | NLP2012-112,NC2012-102 |
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Committee | NLP |
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Conference Date | 2013/1/17(1days) |
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Registration To | Nonlinear Problems (NLP) |
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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 |
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