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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 8 of 8  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
IT 2019-07-26
10:30
Tokyo NATULUCK-Iidabashi-Higashiguchi Ekimaeten Unbiased Estimation Equation for f-Separable Bregman Distortion Measures and the Properties of Its Estimators
Masahiro Kobayashi, Kazuho Watanabe (Toyohashi Tech.) IT2019-22
In this study, we discuss unbiased estimation equations in a class of objective function using the monotonically increas... [more] IT2019-22
pp.37-42
IBISML 2018-11-05
15:10
Hokkaido Hokkaido Citizens Activites Center (Kaderu 2.7) [Poster Presentation] Generalized Dirichlet-Process-Means with f-Mean and Analysis of Influence Function
Masahiro Kobayashi, Kazuho Watanabe (Toyohashi Tech.) IBISML2018-50
DP-means clustering was obtained as an extension of $K$-means clustering. While it is implemented with a simple and effi... [more] IBISML2018-50
pp.45-52
IBISML 2018-11-05
15:10
Hokkaido Hokkaido Citizens Activites Center (Kaderu 2.7) [Poster Presentation] Bregman monotone operator splitting and its application example
Kenta Niwa (NTT), W. Bastiaan Kleijn (VUW) IBISML2018-80
Monotone operator splitting is a powerful paradigm that facilitates parallel processing for optimization problems where ... [more] IBISML2018-80
pp.271-278
IT 2016-12-13
15:50
Gifu Takayama Green Hotel [Invited Talk] Bregman Divergence and its Applications
Takafumi Kanamori (Nagoya Univ.) IT2016-44
In statistical inference and machine learning, Bregman divergences are often used. This paper shows applications of Breg... [more] IT2016-44
pp.15-20
PRMU 2013-12-12
15:50
Mie   Enzyme Active Site Prediction Using Bregman Divergence Regularized Machine
Raissa Relator, Tsuyoshi Kato (Gunma Univ.), Nozomi Nagano (AIST) PRMU2013-78
[more] PRMU2013-78
pp.61-66
IBISML 2012-06-19
11:00
Kyoto Campus plaza Kyoto Online Prediction under Submodular Constraints
Daiki Suehiro, Kohei Hatano, Shuji Kijima, Eiji Takimoto (Kyushu Univ.), Kiyohito Nagano (Tokyo Univ.) IBISML2012-3
 [more] IBISML2012-3
pp.15-22
NC 2011-07-25
13:45
Hyogo Graduate School of Engineering, Kobe University General Framework for Local Variational Approximation of Bayesian Learning Using Bregman Divergence
Kazuho Watanabe (NAIST), Masato Okada (Univ. of Tokyo), Kazushi Ikeda (NAIST) NC2011-25
The local variational method is a technique to approximate an intractable posterior distribution in Bayesian learning. T... [more] NC2011-25
pp.25-30
IBISML 2010-11-04
15:00
Tokyo IIS, Univ. of Tokyo [Poster Presentation] A Unified Framework of Density Ratio Estimation under Bregman Divergence
Masashi Sugiyama (Tokyo Inst. of Tech.), Taiji Suzuki (Univ. of Tokyo), Takafumi Kanamori (Nagoya Univ.) IBISML2010-64
Estimation of the ratio of probability densities has attracted a great deal of attention
since it can be used for addre... [more]
IBISML2010-64
pp.33-44
 Results 1 - 8 of 8  /   
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