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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| RCC, ISEC, IT, WBS | 2024-03-14 17:00 |
Osaka | Osaka Univ. (Suita Campus) | Comparison of Scale Parameter Dependence of Estimation Performance in Sparse Bayesian Linear Regression Model with Variance Gamma Prior Distribution and t-Prior Distribution Kazuaki Murayama (UEC)  IT2023-135 ISEC2023-134 WBS2023-123 RCC2023-117 |
In the sparse estimation with linear regression model, the variance gamma distribution and t-distribution can be used as... [more] | IT2023-135 ISEC2023-134 WBS2023-123 RCC2023-117 pp.374-379 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-03 16:55 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
Upper bound of real log canonical threshold based on linear programming problem for the multi-indexes of a polynomial Joe Hirose (Tokyo Tech) PRMU2022-125 IBISML2022-132 |
A real log canonical threshold (RLCT) is an invariant which gives a Bayesian generalization error. While a strict value ... [more] | PRMU2022-125 IBISML2022-132 pp.363-370 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2022-06-27 14:25 |
Okinawa | (Primary: On-site, Secondary: Online) |
A Bagging Method to Improve the Accuracy of Gaussian Process Regression for Neural Architecture Search Rion Hada, Masao Okita, Fumihiko Ino (Osaka Univ.) NC2022-2 IBISML2022-2 |
The goal of this study is to improve performance estimation for neural network architectures in neural architecture sear... [more] | NC2022-2 IBISML2022-2 pp.6-13 |
| IT, EMM | 2022-05-18 12:40 |
Gifu | Gifu University (Primary: On-site, Secondary: Online) |
On Bayesian Approach for Classification of Context Tree Model Shota Saito (Gunma Univ.) IT2022-11 EMM2022-11 |
This study deals with the Bayesian classification problem, which was investigated by Merhav and Ziv [IEEE Trans. Inf. Th... [more] | IT2022-11 EMM2022-11 pp.56-60 |
| EA, US, SP, SIP, IPSJ-SLP [detail] | 2021-03-03 16:45 |
Online | Online | An optimal prediction of phoneme under Bayes criterion by weighting multiple hidden Markov models Taishi Yamaoka, Shota Saito, Toshiyasu Matsushima (Waseda Univ.) EA2020-76 SIP2020-107 SP2020-41 |
In this paper, we propose a prediction method for prediction problems using a hidden Markov model. Specifically, it is a... [more] | EA2020-76 SIP2020-107 SP2020-41 pp.97-102 |
| IT | 2020-12-02 09:40 |
Online | Online | Approximation Method for Bayes Optimal Prediction in Phoneme Recognition Problem Taishi Yamaoka, Shota Saito, Toshiyasu Matsushima (Waseda Univ.) IT2020-30 |
In this paper, we propose a method of phoneme recognition. In the previous studies on phoneme recognition using the Hidd... [more] | IT2020-30 pp.32-37 |
| IT | 2020-12-02 10:30 |
Online | Online | Error Probability of Classification Based on the Analysis of the Bayes Code
-- Extension and Example -- Shota Saito, Toshiyasu Matsushima (Waseda Univ.) IT2020-32 |
Suppose that we have two training sequences generated by parametrized distributions $P_{theta^*}$ and $P_{xi^*}$, where ... [more] | IT2020-32 pp.44-49 |
| PRMU, IPSJ-CVIM | 2020-03-17 16:50 |
Kyoto | (Cancelled but technical report was issued) |
Experimental Evaluation for Bayes Error Estimation Capability of Large Geometric Margin Minimum Classification Error Training Ikuhiro Nishiyama (Doshisha Univ.), Hideyuki Watanabe (ATR), Shigeru Katagiri, Miho Ohsaki (Doshisha Univ.) PRMU2019-99 |
Previous studies suggested that the Large Geometric Margin-Minimum Classification Error (LGM-MCE) training method had th... [more] | PRMU2019-99 pp.231-236 |
| IBISML | 2020-01-09 13:25 |
Tokyo | ISM | Real Log Canonical Threshold of Three Layered Neural Network with Swish Activation Function Raiki Tanaka, Sumio Watanabe (Tokyo Tech) IBISML2019-19 |
In neural network learning, it is known that selection of activation function effects generalization performance. Althou... [more] | IBISML2019-19 pp.9-15 |
| NC, MBE | 2019-12-06 17:20 |
Aichi | Toyohashi Tech | Regularization Term of WRH Type Used with Moore-Penrose Inverse for Optimizing Neural Networks Yoshifusa Ito (FHU), Hiroyuki Izumi (AGU), Cidambi Srinivasan (UK) MBE2019-60 NC2019-51 |
Weigend et al. proposed an algorithm for optimizing neural networks, which suppressed the notorious over- tting. They at... [more] | MBE2019-60 NC2019-51 pp.89-94 |
| ASN | 2019-01-29 14:25 |
Kagoshima | Kyuukamura Ibusuki | [Poster Presentation]
An Optimization of Drone Flight Plan based on Simulation for Precise Three-Dimensional Reconstruction Tatsuya Kobayashi, Zhang Heming, Shin Kawai, Hajime Nobuhara (Univ. Tsukuba) ASN2018-95 |
In the present three-dimensional reconstruction scheme using drone, various parameters such as the photographing positio... [more] | ASN2018-95 pp.89-93 |
| AI | 2018-08-27 15:50 |
Osaka | Bayesian Inference for Field of Physical Quantity from Data obtained at several Locations Masato Ota, Takeshi Okadome (KG Univ.) AI2018-23 |
This paper proposes a novel method for estimating the physical quantity at every location (physical quan- tity field) fr... [more] | AI2018-23 pp.55-60 |
|
| MBE, NC (Joint) |
2018-03-14 10:25 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Experimental Analysis of Real Log Canonical Threshold in Stochastic Matrix Factorization using Hamiltonian Monte Carlo Method Naoki Hayashi, Sumio Watanabe (Tokyo Tech) NC2017-89 |
For the real log canonical threshold (RLCT) that gives the Bayesian generalization error of stochastic matrix factorizat... [more] | NC2017-89 pp.127-131 |
| EA, ASJ-H | 2017-11-30 11:00 |
Overseas | University of Auckland (New Zealand) | Fuzzy Bayesian Filter for Sound Environment by Considering Additive Property of Energy Variable and Fuzzy Observation in Decibel Scale Akira Ikuta (Prefectural Univ. of Hiroshima), Hisako Orimoto (Prefectural Univ. or Hiroshima) EA2017-61 |
In the measurement and evaluation of actual random phenomena in a sound environment system, the observed data often cont... [more] | EA2017-61 pp.19-24 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | [Poster Presentation]
Real Log Canonical Threshold of Stochastic Matrix Factorization and its Application to Bayesian Learning Naoki Hayashi, Sumio Watanabe (TokyoTech) IBISML2017-38 |
In stochastic matrix factorization (SMF), we deal with problems that we predict an observed stochastic matrix as a produ... [more] | IBISML2017-38 pp.23-30 |
| MBE, NC (Joint) |
2017-03-13 10:00 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Experimental Analysis of Real Log Canonical Threshold in Non-negative Matrix Factorization Naoki Hayashi, Sumio Watanabe (Tokyo Tech) NC2016-78 |
For the real log canonical threshold ( RLCT ) that gives the Bayesian generalization error of non-negative matrix factor... [more] | NC2016-78 pp.85-90 |
| IT, SIP, RCS | 2017-01-20 11:15 |
Osaka | Osaka City Univ. | Performance of L1 regularized channel estimation techniques using information criteria Yasuhiro Takano (Kobe Univ.) IT2016-85 SIP2016-123 RCS2016-275 |
Most $ell1$ regularized channel estimation techniques assume that degree of sparsity (DoS) is known. The variance of cha... [more] | IT2016-85 SIP2016-123 RCS2016-275 pp.227-230 |
| CCS | 2015-11-10 15:00 |
Kyoto | Inamori Foundation Memorial Building, Kyoto Univ. | Segmental Bayesian estimation of neuronal parameters from spike trains Isao Tokuda, Huu Hoang (Ritsumeikan Univ.) CCS2015-63 |
Multi-electrode recording is now a common technique to simultaneously collect neuronal spike data of a population of the... [more] | CCS2015-63 pp.99-102 |
| PRMU | 2013-06-11 16:00 |
Tokyo | Criterion for image stitching based on the intensity distribution and entropy Kenta Matsui, Kazuaki Kondo, Takahiro Koizumi, Yuichi Nakamura (Kyoto Univ.) PRMU2013-32 |
In this paper, we propose a criteria of image stitching to acquire larger panoramic images from first person view videos... [more] | PRMU2013-32 pp.77-82 |
|
| 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 |
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