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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 #
CNR, BioX 2022-03-03
13:00
Online Online FMR estimation of facial recognition systems using extreme value theory
Hikotaro Suzuki, Mitsutoshi Himaga (JAISA) BioX2021-46 CNR2021-27
The accuracy of biometric systems has been improved dramatically in the past decade. As the number of samples required f... [more] BioX2021-46 CNR2021-27
pp.1-6
CNR, BioX 2022-03-03
13:30
Online Online Evaluation of Biometric Accuracy with Small Samples Using Extreme Value Distribution
Tomoaki Matsunami, Shigefumi Yamada (JAISA) BioX2021-47 CNR2021-28
The accuracy of recent biometrics technology has been improved, and it is necessary to valuate at very small False Match... [more] BioX2021-47 CNR2021-28
pp.7-12
SDM, ICD, ITE-IST [detail] 2019-08-08
10:00
Hokkaido Hokkaido Univ., Graduate School /Faculty of Information Science and Application of Extreme Value Theory to Statistical Analyses of Worst Case SRAM Data Retention Voltage
Tomoko Mizutani, Kiyoshi Takeuchi, Takuya Saraya, Masaharu Kobayashi, Toshiro Hiramoto (Univ. of Tokyo) SDM2019-41 ICD2019-6
The extreme value theory was applied to the estimation of the maximum SRAM data retention voltage (DRV). It was found th... [more] SDM2019-41 ICD2019-6
pp.27-30
RCS, NS
(Joint)
2013-12-19
09:25
Kagawa Sunport Hall Takamatsu Extreme value analysis for large scale outages of telecommunication networks
Hiroyuki Funakoshi, Tatsuya Matsukawa (NTT) NS2013-136
In telecommunication networks, it is difficult to predict the probability of occurrence of large-scale outages because t... [more] NS2013-136
pp.13-18
IN, NS
(Joint)
2009-03-04
09:00
Okinawa Okinawa-Zanpamisaki Royal Hotel Analysis of threshold network model based on extreme value theory and universal power laws
Akihiro Fujihara (Kwansei Gakuin Univ.), Masato Uchida (Kyushu Inst. of Tech.), Hiroyoshi Miwa (Kwansei Gakuin Univ./NICT) IN2008-176
There is a threshold model known as one of important stochastic
models to understand the structure of complex networks... [more]
IN2008-176
pp.261-266
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