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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MI | 2012-07-19 15:40 |
Yamagata | Yamagata Univ. | Bayesian Inference Approach to Visualize Neuroreceptor Density using Positron Emission Tomography without Arterial Blood Sampling Takahiro Kozawa, Hidekata Hontani (NIT), Kazuya Sakaguchi (Kitasato Univ), Muneyuki Sakata (TMGHIG), Yuichi Kimura (NIRS)  MI2012-26 |
A Bayesian approach to de-noise tissue time activity curves (tTAC) is proposed in order to quantitatively visualize neur... [more] | MI2012-26 pp.29-34 |
| EMM, ISEC, SITE, ICSS, IPSJ-CSEC, IPSJ-SPT [detail] | 2012-07-19 13:30 |
Hokkaido | Performance evaluation of digital watermarking model with image restoration
-- image restoration using 2D Ising model -- Masaki Kawamura (Yamaguchi Univ.), Tatsuya Uezu (Nara Women's Univ.), Masato Okada (Univ. Tokyo) ISEC2012-13 SITE2012-9 ICSS2012-15 EMM2012-5 |
We evaluate the decoding performance in the case that the prior probability is given by 2D Ising model in a spread spect... [more] | ISEC2012-13 SITE2012-9 ICSS2012-15 EMM2012-5 pp.29-34 |
|
| MBE, NC (Joint) |
2012-03-14 16:50 |
Tokyo | Tamagawa University | Bayesian Network Associative Memories Hiroaki Hasegawa, Masafumi Hagiwara (Keio Univ.) NC2011-146 |
In this paper, we propose Bayesian Network Associative Memories (BNAMs) for modeling associative memories with Bayesian ... [more] | NC2011-146 pp.147-152 |
| NC, MBE (Joint) |
2011-03-08 09:50 |
Tokyo | Tamagawa University | A Theoretical Analysis of KL-type Generalization Error on Hidden Variable Distribution Keisuke Yamazaki (Tokyo Inst. of Tech.) NC2010-165 |
In information science, hierarchical models such as mixture models, hidden Markov models and Bayesian networks are wide... [more] |
NC2010-165 pp.223-228 |
| NC, MBE (Joint) |
2011-03-08 10:40 |
Tokyo | Tamagawa University | Effect of Information Source on Cross Validation in Variational Bayes Learning Shinji Oyama, Sumio Watanabe (Tokyo Tech.) NC2010-167 |
The variational Bayes learning provides high generalization performance as the Bayes learning using a small computationa... [more] | NC2010-167 pp.235-240 |
| MBE | 2010-10-14 14:45 |
Osaka | Osaka Electro-Communication University | Sequential Error Rate Evaluation of EEG:SSVEP Binary Classification Problem
-- Bayesian Sequential Learning with Sequential Monte Carlo method -- Hideyuki Hara (Waseda Univ.), Atsushi Takemoto (Kyoto Univ.), Yumi Dobashi (Waseda Univ.), Katsuki Nakamura (Kyoto Univ.), Takashi Matsumoto (Waseda Univ.) MBE2010-31 |
An attempt was made to evaluate the \textit{Sequential Error Rate} (SER) of an SSVEP classification problem with a Bayes... [more] | MBE2010-31 pp.17-22 |
| 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, MBE (Joint) |
2010-03-09 14:35 |
Tokyo | Tamagawa University | Localization of Robots Based on Learning of Filters for Image features Mariko Oki, Masumi Ishikawa (Kyushu Inst. of Tech.) NC2009-107 |
In feature-based localization of a mobile robot, it is difficult to decide what features to use for localization.To trai... [more] | NC2009-107 pp.113-118 |
| AI, JSAI-KBS, JSAI-SAI, IPSJ-ICS | 2010-03-01 - 2010-03-03 |
Hokkaido | Page Ranking System Using Bayesian Filter Masayoshi Niwano, Kenneth James Mackin, Yasuo Nagai (Tokyo Univ. of Info Sci.) AI2009-45 |
In this paper, we proposed Web page ranking system that considers user’s preference information by using the Bayesian fi... [more] | AI2009-45 pp.17-22 |
|
| 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) |
2009-12-11 17:00 |
Aichi | On the estimating method of the information rates of retinal ganglion cells Hiroki Saito, Yoshimi Kamiyama (Aichi Prefectural Univ) NC2009-69 |
Mutual information is often used to quantify the response property of retinal ganglion cells to sensory inputs. To calcu... [more] | NC2009-69 pp.37-42 |
|
| NC | 2009-10-24 10:40 |
Saga | Saga University | Mean-field theoretical approach to Bayesian estimation of motion velocity vector in successive digital images Yuya Inagaki, Jun-ichi Inoue (Hokkaido Univ.) NC2009-44 |
We examine a mean-field iterative aigorithm to estimate motion velocity vector fields in successive digital images based... [more] | NC2009-44 pp.41-46 |
| PRMU | 2009-03-13 15:20 |
Miyagi | Tohoku Institute of Technology | A Proposal of Ensemble-based Minimum Classification Error Training Hideyuki Watanabe (NICT/ATR), Shigeru Katagiri, Kohta Yamada (Doshisha Univ.), Atsushi Nakamura, Erik McDermott, Shinji Watanabe (NTT), Shin'ichi Taniguchi, Naho Nishijima, Miho Ohsaki (Doshisha Univ.) PRMU2008-250 |
We propose an ensemble-based minimum classification error (MCE) training method to combine multiple weak classifiers in ... [more] | PRMU2008-250 pp.71-76 |
| NC | 2009-01-19 13:30 |
Hokkaido | Hokkaido Univ. | Structure estimation using time-dependent data in hidden Markov models Masashi Matsumoto, Sumio Watanabe (Tokyo Inst. of Tech.) NC2008-86 |
A lot of learning machines used in information science, for example, mixture models, artificial neural networks, Bayesia... [more] | NC2008-86 pp.25-30 |
| 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 | 2009-01-19 14:20 |
Hokkaido | Hokkaido Univ. | Experimental Study of Bayesian Learning using Langevin Equation in Singular Learing Machines Taruhi Iwagaki, Sumio Watanabe (Tokyo Inst. of Tech.) NC2008-88 |
Langevin equation implies an algorithm that could make samples from the stationary distribution of a biased random walk ... [more] | NC2008-88 pp.37-42 |
| MBE, NC (Joint) |
2007-12-22 10:50 |
Aichi | Generalization and Trainining Errors of Bayes and Gibbs Estimations in Singular Sumio Watanabe (Tokyo Inst. of Tech.) NC2007-75 |
In singular learning machines such as neural networks, normal mixtures, Bayesian networks, and reduced rank regressions... [more] |
NC2007-75 pp.25-30 |
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| PRMU | 2007-12-14 15:30 |
Hyogo | Kobe Univ. | Relationship Between Errors of Supplementary Information and Misrecognition Rates Yoshio Furuya, Masakazu Iwamura, Koichi Kise (Osaka Pref. Univ.), Shinichiro Omachi (Tohoku Univ.), Seiichi Uchida (Kyusyu Univ.) PRMU2007-152 |
Pattern recognition with supplementary information is a new pattern recognition framework that determines an output clas... [more] | PRMU2007-152 pp.95-100 |
| 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 |
| PRMU | 2006-03-16 14:55 |
Fukuoka | Kyushu Univ. | Bayesian Face Recognition using a Sequential Monte Carlo Method Atsushi Matsui, Simon Clippingdale, Mahito Fujii, Nobuyuki Yagi (NHK) |
We introduce a sequential learning algorithm for Bayesian probability distributions describing faces in video input imag... [more] | PRMU2005-250 pp.119-124 |
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