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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
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Committee Date Time Place Paper Title / Authors Abstract Paper #
PRMU, IPSJ-CVIM 2020-03-17
16:50		 Kyoto
(Cancelled but technical report was issued)		 Experimental Evaluation for Bayes Error Estimation Capability of Large Geometric Margin Minimum Classification Error Training
Ikuhiro Nishiyama (Doshisha Univ.), Hideyuki Watanabe (ATR), Shigeru Katagiri, Miho Ohsaki (Doshisha Univ.) PRMU2019-99		 Previous studies suggested that the Large Geometric Margin-Minimum Classification Error (LGM-MCE) training method had th... [more] PRMU2019-99
pp.231-236
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 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
PRMU, IPSJ-CVIM, MVE [detail] 2015-01-23
10:15		 Nara   Relation between Data Grouping and Robustness to Unseen Data in Large Geometric Margin Minimum Classification Error Training
Hiroyuki Shiraishi (Doshisha Univ), Hideyuki Watanabe (NICT), Shigeru Katagiri (Doshisha Univ), Xugang Lu, Chiori Hori (NICT), Miho Ohsaki (Doshisha Univ) PRMU2014-101 MVE2014-63		 To develop a pattern classifier that is robust to unseen pattern samples, classifier parameters have been conventionally... [more] PRMU2014-101 MVE2014-63
pp.177-182
PRMU, IPSJ-CVIM, MVE [detail] 2014-01-23
11:00		 Osaka   Large Geometric Margin Minimum Classification Error Training with Automatic Optimization Of The Number of Prototypes
Yuji Takayama (Doshisha Univ.), Hideyuki Watanabe (NICT), Shigeru Katagiri, Miho Ohsaki (Doshisha Univ.), Shigeki Matsuda, Chiori Hori (NICT) PRMU2013-94 MVE2013-35		 Large Geometric Margin Minimum Classification Error (LGM-MCE) training, which adopts geometric-margin-based misclassific... [more] PRMU2013-94 MVE2013-35
pp.19-24
IE, SIP, ICD, VLD, IPSJ-SLDM [detail] 2012-10-19
14:30		 Iwate Hotel Ruiz CMOS Op-amp Circuit Synthesis with Geometric Programming
Yu Zhang, Qing Dong, Shigetoshi Nakatake (Univ. of Kitakyushu), Bo Yang, Jing Li (Design Algorithm Lab.) VLD2012-54 SIP2012-76 ICD2012-71 IE2012-78		 This work presents a 6T SRAM design in nanometer process via geometric programming (GP). We adopt the transistor array (... [more] VLD2012-54 SIP2012-76 ICD2012-71 IE2012-78
pp.77-82
PRMU 2011-03-11
09:30		 Ibaraki   Application of Automatic Loss Smoothness Control to Large Geometric Margin Minimum Classification Error Training
Tsukasa Ohashi (Doshisha Univ.), Hideyuki Watanabe (NICT), Jun'ichi Tokuno, Shigeru Katagiri, Miho Ohsaki (Doshisha Univ.) PRMU2010-270		 A method that automatically controls the smoothness of a smoothed classification error count loss using Parzen estimatio... [more] PRMU2010-270
pp.195-200
PRMU, FM 2010-12-09
16:40		 Yamaguchi   Large Geometric Margin Minimum Classification Error Training for Kernel-based High Dimensional Space
Hideyuki Watanabe (NICT), Shigeru Katagiri, Mamoru Adachi, Miho Ohsaki (Doshisha Univ.) PRMU2010-136		 Large Geometric Margin Minimum Classification Error (LGM-MCE) training has been successfully applied to multi-class clas... [more] PRMU2010-136
pp.55-60
PRMU 2009-08-31
10:00		 Miyagi Tohoku Univ. Geometric Margin for a General Class of Discriminant Functions and Its Control for Minimum Error Classification
Hideyuki Watanabe (NICT), Shigeru Katagiri, Kouta Yamada (Doshisha Univ.), Erik McDermott, Atsushi Nakamura, Shinji Watanabe (NTT Corp.), Miho Ohsaki (Doshisha Univ.) PRMU2009-60		 The recent dramatic growth of computation power has resulted in increased interests in discriminative training (DT) meth... [more] PRMU2009-60
pp.1-6
SP 2009-07-17
13:50		 Fukushima   Geometric Margin Control for Minimum Error Classification
Kouta Yamada, Shigeru Katagiri (Doshisha Univ.), Erik McDermott (NTT), Hideyuki Watanabe (NICT), Atsushi Nakamura, Shinji Watanabe (NTT), Miho Ohsaki (Doshisha Univ.) SP2009-43		 The recent dramatic growth of computation power and data availability has increased research interests in discriminative... [more] SP2009-43
pp.13-18
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