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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 41 - 57 of 57 [Previous]  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
IBISML 2010-11-05
15:30
Tokyo IIS, Univ. of Tokyo [Poster Presentation] On the difference between Bayes and Variational Bayes in a normal mixture
Tetsutaro Yamada, Sumio Watanabe (Tokyo Tech.) IBISML2010-84
Variational Bayes method or mean field approximation
is widely used because it enables us to estimate the information s... [more]
IBISML2010-84
pp.181-186
IBISML 2010-11-05
15:30
Tokyo IIS, Univ. of Tokyo [Poster Presentation] Global Analytic Solution for Variational Bayesian Matrix Factorization and its Model-induced Regularization
Shinichi Nakajima (Nikon), Masashi Sugiyama (Tokyo Inst. of Tech.), Ryota Tomioka (Univ. of Tokyo) IBISML2010-99
Bayesian methods of matrix factorization (MF) have been actively explored recently
as promising alternatives to classic... [more]
IBISML2010-99
pp.291-302
IBISML, PRMU, IPSJ-CVIM [detail] 2010-09-06
09:00
Fukuoka Fukuoka Univ. A Study of Variances of Cross-Validation and Generalization Error in Variational Bayes Method
Shinji Oyama, Sumio Watanabe (Tokyo Tech.) PRMU2010-74 IBISML2010-46
Variational Bayes method provides high generalization performance as the Bayes method using a small computational cost a... [more] PRMU2010-74 IBISML2010-46
pp.135-142
NC 2010-07-27
14:55
Kyoto Kyoto University Hierarchical Bayes method for NIRS-DOT inverse problem and its phase diagrams
Atsushi Miyamoto, Kazuho Watanabe, Kazushi Ikeda (NAIST), Masa-aki Sato (ATR) NC2010-38
The NIRS-DOT is a method to reconstruct tomographic images from the data by solving the linear equations, which have amb... [more] NC2010-38
pp.51-56
NC, MBE
(Joint)
2010-03-10
14:10
Tokyo Tamagawa University Variational Bayes approach for NIRS-DOT inverse problem
Atsushi Miyamoto, Kazuho Watanabe, Kazushi Ikeda (NAIST), Masa-aki Sato (ATR) NC2009-137
NIRS-DOT is tomography which is reconstructed from NIRS data. NIRS-DOT requires to solve the inverse problem which has i... [more] NC2009-137
pp.291-296
PRMU, SP, MVE, CQ 2010-01-21
11:40
Kyoto Kyoto Univ. Online speaker clustering using an ergodic HMM and its application to meeting minute generation
Takafumi Koshinaka, Kentaro Nagatomo, Kenji Satoh (NEC Corp.) CQ2009-62 PRMU2009-161 SP2009-102 MVE2009-84
A novel online speaker clustering method suitable for real-time applications is proposed. Using an ergodic hidden Marko... [more] CQ2009-62 PRMU2009-161 SP2009-102 MVE2009-84
pp.39-44
NC, MBE
(Joint)
2009-03-13
09:20
Tokyo Tamagawa Univ. Modular Reinforcement Learning based on Adaptive Model Complexity
Yu Hiei (Nara Inst. of Sci and Tech.), Takeshi Mori (Kyoto Univ.), Shin Ishii (Kyoto Univ./Nara Institute of Science and Technology) NC2008-149
In real-world problems such as robot control, the environment surrounding a controlled system is nonstationary, and the ... [more] NC2008-149
pp.273-278
PRMU 2009-02-20
11:15
Tokyo Univ. of Tokyo (IIS) Automatically Estimating Number of Scenes for Video Summarization using Model Selection Criteria
Koji Yamasaki, Koichi Shinoda, Sadaoki Furui (Tokyo Inst. of Tech.) PRMU2008-231
This paper describes a video summarization system using model selection techniques to estimate the optimal number of sce... [more] PRMU2008-231
pp.139-144
NC 2009-01-19
13:55
Hokkaido Hokkaido Univ. On the Effect of Hyperparameter to Generalization Error in Variational Bayes Learning
Shinji Oyama, Sumio Watanabe (Tokyo Inst. of Tech.) NC2008-87
In variational Bayes learning, the probability distribution of the hidden variable and parameter is made by the mean fie... [more] NC2008-87
pp.31-36
NC, MBE
(Joint)
2008-12-20
10:00
Aichi Nagoya Inst. Tech. Clustering complex networks with the prior based on degree distribution
Naoyuki Harada, Ichiro Takeuchi (NIT), Ryohei Nakano (Chubu Univ.) NC2008-73
Newman et al. proposed a graph clustering method based on a robabilistic mixture model with only the general assumption ... [more] NC2008-73
pp.1-6
NC, MBE
(Joint)
2008-12-20
11:05
Aichi Nagoya Inst. Tech. A Study on Variational Bayes Method with the Primitive Initial Point
Yuta Ishikawa, Ichiro Takeuchi (NIT), Ryohei Nakano (Chubu Univ.) NC2008-75
The variational bayes (VB) method is widely used as an approximation of
the bayes method.
Since the objective functio... [more]
NC2008-75
pp.13-18
HIP 2008-03-22
- 2008-03-23
Fukuoka Kitakyushu Science and Research Park Object Concept Modeling Based on Relationship among Visual Information, Usage and Function
Yasuhito Shinchi, Takayuki Nagai (UEC) HIP2007-159
A novel object concept model, which encodes relationship among visual information, function and usage, is proposed in th... [more] HIP2007-159
pp.1-6
SIS 2008-03-14
13:10
Tokyo Musashi Institute of Technology(Setagaya) A Model of Tools Usage Based on Visual Observation
Yasuhito Shichi, Takayuki Nagai (UEC) SIS2007-86
Recognizing object function is a very important aspect of object recognition tasks as well as recognizing object visuall... [more] SIS2007-86
pp.27-32
NC 2007-10-18
09:55
Miyagi Tohoku University Variational Bayes Hidden Markov Models for extracting spatiotemporal spike pattern
Kentaro Katahira (Univ. Tokyo/RIKEN), Jun Nishikawa, Kazuo Okanoya (RIKEN), Masato Okada (Univ. Tokyo/RIKEN) NC2007-34
Hidden Markov Model (HMM) is used to extracting spatio-temporal pattern from spikes recorded by
multielectrode. The EM ... [more]
NC2007-34
pp.7-12
NC 2007-03-14
15:10
Tokyo Tamagawa University Markov and semi-Markov switching of source appearances for non-stationary independent component analysis
Junichiro Hirayama (NAIST/JSPS), Shin-ichi Maeda, Shin Ishii (NAIST)
Independent Component Analysis (ICA) is currently the most popularly used approach to blind source separation (BSS), the... [more] NC2006-147
pp.173-178
NC 2007-03-15
15:30
Tokyo Tamagawa University Deterministic Annealing in Variational Baysian Algorithm
Kentaro Katahira (Univ. Tokyo/RIKEN), Kazuho Watanabe (Tokyo Tech), Masato Okada (Univ. Tokyo/RIKEN)
Variational Bayes (VB) algorithm is widely used as an approximation of Bayesian method. The VB algorithm can approximate... [more] NC2006-183
pp.177-182
NC 2006-03-15
11:00
Tokyo Tamagawa University Analysis of Hierarchical Variational Bayes Approach in Linear Inverse Problem
Shinichi Nakajima, Sumio Watanabe (Tokyo Inst. of Tech./Nikon)
It is known that,
in singular models,
the Bayes estimation commonly has the advantage of generalization performance
o... [more]
NC2005-117
pp.67-72
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