Presentation 2012-11-07
A Proposal of Adaptive Metric Learning Using Category Information for Text Classification
Kenta MIKAWA, Takashi ISHIDA, Masayuki GOTO, Shigeichi HIRASAWA,
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Abstract(in English) Extended cosine measure has been proposed as one of the method of metric learning which learns metric matrix expressing the characteristics of training data. However, this method introduces a unique metric matrix and estimate it by learning of all training data. Therefore, there is a room to improve this method because document data has normally different statistical characteristics in each category. In this study, we propose the way of learning metric matrices for each category. To show the effectiveness of our proposed method, simulation experiment is conducted.
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Keyword(in English) Metric Learning / Extended Cosine Measure / Vector Space Model / Text Classification
Paper # IBISML2012-45
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Committee IBISML
Conference Date 2012/10/31(1days)
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Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) A Proposal of Adaptive Metric Learning Using Category Information for Text Classification
Sub Title (in English)
Keyword(1) Metric Learning
Keyword(2) Extended Cosine Measure
Keyword(3) Vector Space Model
Keyword(4) Text Classification
1st Author's Name Kenta MIKAWA
1st Author's Affiliation Graduate School of Creative Science and Engineering, Waseda University()
2nd Author's Name Takashi ISHIDA
2nd Author's Affiliation Madia Network Center, Waseda University
3rd Author's Name Masayuki GOTO
3rd Author's Affiliation School of Creative Science and Engineering, Waseda University
4th Author's Name Shigeichi HIRASAWA
4th Author's Affiliation Research Institute for Science and Engineering, Waseda University
Date 2012-11-07
Paper # IBISML2012-45
Volume (vol) vol.112
Number (no) 279
Page pp.pp.-
#Pages 6
Date of Issue