Presentation 2010-12-10
Comparison between Minimum Classification Error Training and a Support Vector Machine in Prototype-based Classifier Design
Mamoru ADACHI, Hideyuki WATANABE, Shigeru KATAGIRI, Miho OHSAKI,
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Abstract(in English) In this paper, we experimentally compared the size of trained classifier class models (prototypes) between Support Vector Machine (SVM) and a newly proposed discriminative training method called Large Geometric Margin Minimum Classification Error (LGM-MCE) training. We first clarify that SVM often requires a large number of prototypes (i.e., support vectors), which result in a scalability problem. Next we show the insufficiency of SVM when the number of support vectors is limited and clearly demonstrate that LGM-MCE is much more useful for realizing a small-size but highly-discriminative classifier than SVM.
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Keyword(in English) Minimum classification error training / Support Vector Machine / Prototype-based classifier / K-means clustering
Paper # PRMU2010-146
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Committee PRMU
Conference Date 2010/12/2(1days)
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Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Comparison between Minimum Classification Error Training and a Support Vector Machine in Prototype-based Classifier Design
Sub Title (in English)
Keyword(1) Minimum classification error training
Keyword(2) Support Vector Machine
Keyword(3) Prototype-based classifier
Keyword(4) K-means clustering
1st Author's Name Mamoru ADACHI
1st Author's Affiliation Doshisha University()
2nd Author's Name Hideyuki WATANABE
2nd Author's Affiliation MASTAR Project, NICT
3rd Author's Name Shigeru KATAGIRI
3rd Author's Affiliation Doshisha University
4th Author's Name Miho OHSAKI
4th Author's Affiliation Doshisha University
Date 2010-12-10
Paper # PRMU2010-146
Volume (vol) vol.110
Number (no) 330
Page pp.pp.-
#Pages 6
Date of Issue