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Committee Date Time Place Paper Title / Authors Abstract Paper #
IBISML 2023-09-08
13:25		 Osaka Osaka Metropolitan University (Nakamozu Campus)
(Primary: On-site, Secondary: Online)		 Consideration of Negative Samples in Contrastive Learning
Daiki Ishiguro, Tomoko Ozeki (Tokai Univ.) IBISML2023-28		 Contrastive learning has achieved accuracy comparable to supervised learning. In this method, the transformed image pair... [more] IBISML2023-28
pp.16-21
MBE, NC
(Joint)		 2013-03-15
11:05		 Tokyo Tamagawa University Improvement of the relaxation procedure in concurrent Q learning
Kazunori Murakami, Tomoko Ozeki (Tokai Univ.) NC2012-170		 In this study, we point out problems and suggest the modification of relaxation in CQL-e which is one of the adaptation ... [more] NC2012-170
pp.209-213
IBISML 2012-11-08
15:00		 Tokyo Bunkyo School Building, Tokyo Campus, Tsukuba Univ. Adaptive Ability of Concurrent Q Learning, Sarsa and Q Learning to Dinamical Environment
Kazunori Murakami, Tomoko Ozeki (Tokai Univ.) IBISML2012-67		 In this paper, we compare the performance of Sarsa, Q learning and concurrent Q learning, one of the adaptive techniques... [more] IBISML2012-67
pp.237-241
IBISML 2010-11-04
15:00		 Tokyo IIS, Univ. of Tokyo [Poster Presentation] Index Type Full-Text Search Support System Using Self-Organizing Map
Takahito Kuniyoshi, Tomoko Ozeki (Tokai Univ.) IBISML2010-78		 This study is aimed for imprevent of the search efficiency by performing a dynamic classification for matched pages to q... [more] IBISML2010-78
pp.147-150
IBISML 2010-11-05
15:30		 Tokyo IIS, Univ. of Tokyo [Poster Presentation] Improvement of accuracy in biometrics authentication by dummy data
Akihiro Nakajima, Tomoko Ozeki (Tokai Univ.) IBISML2010-82		 In this paper, we propose a new method to improve the accuracy in biometrics authentication by adding dummy data. Becaus... [more] IBISML2010-82
pp.169-174
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