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Committee Date Time Place Paper Title / Authors Abstract Paper #
PRMU 2018-12-14
15:50		 Miyagi   Bayes Boundary Estimation Capability Assessment for Large Geometric Margin Minimum Classification Error Training
Ikuhiro Nishiyama (Doshisha Univ.), Hideyuki Watanabe (ATR), Shigeru Katagiri, Miho Osaki (Doshisha Univ.) PRMU2018-92		 The recent, Large Geometric Margin Minimum Classification Error training has, based on the smoothness of its smooth clas... [more] PRMU2018-92
pp.91-96
PRMU, CNR 2018-02-20
11:45		 Wakayama   A Classification-Uncertainty-Based Criterion for Classification Boundary Selection
David Ha (Doshisha Univ.), Juliette Maes (ECL), Yuya Tomotoshi (Doshisha Univ.), Hideyuki Watanabe (ATR), Shigeru Katagiri, Miho Osaki (Doshisha Univ.) PRMU2017-166 CNR2017-44		  [more] PRMU2017-166 CNR2017-44
pp.121-126
PRMU, CNR 2017-02-18
11:20		 Hokkaido   Small-sized Kernel Classifier By Support Vector Retraining Based on Minimum Classification Error Criterion
Ryoma Tani (Doshisha Univ.), Hideyuki Watanabe (ATR), Shigeru Katagiri, Miho Osaki (Doshisha Univ.) PRMU2016-159 CNR2016-26		 Different from the Multi-class Support Vector Machine (MSVM) that fixes Support Vectors (SVs) to training samples, the K... [more] PRMU2016-159 CNR2016-26
pp.41-46
PRMU 2015-12-21
09:30		 Nagano   Evaluation of Automatic Prototype-Model Size Optimization in Large Geometric Margin Minimum Classification Error Training
Masahiro Ogino (Doshisha Univ.), Hideyuki Watanabe (NICT), Shigeru Katagiri, Miho Osaki (Doshisha Univ.), Xugang Lu, Hisashi Kawai (NICT) PRMU2015-100		 To develop a method for nding an appropriate class model size, which leads to accurate classi cation over unseen patter... [more] PRMU2015-100
pp.1-6
 Results 1 - 4 of 4  /   
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