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Committee Date Time Place Paper Title / Authors Abstract Paper #
PRMU, IPSJ-CVIM, MVE [detail] 2014-01-23
09:30		 Osaka   Minimum Classification Error Training with Automatic Determination of Loss Smoothness Common to All Classes
Kensuke Ota (Doshisha Univ.), Hideyuki Watanabe (NICT), Shigeru Katagiri, Miho Ohsaki (Doshisha Univ.), Shigeki Matsuda, Chiori Hori (NICT) PRMU2013-91 MVE2013-32		 The smoothness of the smooth classification error count loss used in the Minimum Classification Error (MCE) training has... [more] PRMU2013-91 MVE2013-32
pp.1-6
IBISML, PRMU, IPSJ-CVIM [detail] 2010-09-06
10:40		 Fukuoka Fukuoka Univ. Minimum Classification Error Training with Automatic Control of Loss Smoothness
Junichi Tokuno (Doshisha Univ.), Hideyuki Watanabe (NICT), Shigeru Katagiri, Miho Ohsaki (Doshisha Univ.) PRMU2010-80 IBISML2010-52		 The smoothness embedded in the Minimum Classification Error formalization has an effect of increasing virtual training s... [more] PRMU2010-80 IBISML2010-52
pp.179-184
PRMU 2009-03-14
15:30		 Miyagi Tohoku Institute of Technology Object Segmentation Based on Adaptive Background Model Considering Spatio-temporal Features
Tatsuya Tanaka, Atsushi Shimada (Kyushu Univ.), Daisaku Arita (ISIT), Rin-ichiro Taniguchi (Kyushu Univ.) PRMU2008-282		 We propose a new method for background modeling considering spatio-temporal features. Our method consists of three compl... [more] PRMU2008-282
pp.281-286
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