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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| PRMU | 2022-12-16 14:10 |
Toyama | Toyama International Conference Center (Primary: On-site, Secondary: Online) |
Sampling Strategies in Data Pruning Ryota Higashi, Toshikazu Wada (Wakayama Univ.)  PRMU2022-48 |
Data Pruning is a method of selecting the training data out of an entire training dataset so as to keep the accuracy aft... [more] | PRMU2022-48 pp.85-90 |
| NC, MBE (Joint) |
2022-09-30 11:35 |
Miyagi | Tohoku Univ. (Primary: On-site, Secondary: Online) |
A dynamic state-space reinforcement learning model explaining functional differentiation of higher motor areas in the cerebral cortex Naoki Tamura, Hajime Mushiake (Tohoku Univ), Kazuhiro Sakamoto (TMPU) NC2022-42 |
Complex and sequential behaviors based on various cues depend on the frontal higher motor areas of the cerebral cortex. ... [more] | NC2022-42 pp.44-48 |
| MI, MICT [detail] | 2021-11-05 14:25 |
Online | Online | A Basic Study of Frequency-Selection Criterions at Adaptive Pre-Filter of Discrete Wavelet Transform Method for Heart Rate Estimation Using mm-Wave Radar Shunsuke Sato, Yaokun Hu, Takeshi Toda (Nihon Univ.) MICT2021-38 MI2021-36 |
We so far have been investigating methods for discrete wavelet transform (DWT) for estimating heart rate frequency using... [more] | MICT2021-38 MI2021-36 pp.49-54 |
| IBISML | 2021-03-03 14:00 |
Online | Online | Learning coefficients of normal mixture models in one dimension. Genki Watanabe, Ryuji Ito, Miki Aoyagi (Nihon Univ.) IBISML2020-48 |
Hierarchical learning models are widely used for data analysis in image or speech recognition, economics and so on. How... [more] | IBISML2020-48 pp.40-46 |
| SeMI, RCS, NS, SR, RCC (Joint) |
2019-07-11 10:40 |
Osaka | I-Site Nanba（Osaka） | [Poster Presentation]
A Study on Coded Modulation Using Autoencoders Akiho Nakata, Koji Ishii (Kagawa Univ.) RCC2019-24 NS2019-60 RCS2019-117 SR2019-36 SeMI2019-33 |
This study tries to design a coded modulation scheme with a desired transmission rate by use of autoencoder and evaluate... [more] | RCC2019-24 NS2019-60 RCS2019-117 SR2019-36 SeMI2019-33 pp.67-72(RCC), pp.93-98(NS), pp.89-94(RCS), pp.99-104(SR), pp.81-86(SeMI) |
| SR | 2019-01-24 16:10 |
Fukushima | Corasse, Fukushima city (Fukushima prefecture) | A Routing Algorithm using machine learning for Wireless Mesh Networks Genki Komuro, Hiroyuki Suzuki, Akio Koyama (Yamagata University) SR2018-107 |
In recent years, attention has been focused on a Wireless Mesh Networks (WMN) that can expand a wireless area with the s... [more] | SR2018-107 pp.71-77 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Active Learning in Sparse Linear Regression Models via Selective Inference Yuta Umezu (NIT), Ichiro Takeuchi (NIT/NIMS/RIKEN) IBISML2018-95 |
In order to efficiently estimate interested parameter, one can design sampling strategy by defining some criterion on th... [more] | IBISML2018-95 pp.381-388 |
| WBS, IT, ISEC | 2018-03-08 10:50 |
Tokyo | Katsusika Campas, Tokyo University of Science | Digital watermark using deep-learning classification Mashu Sakai, Taiki Shigeta, Hikaru Morita (Kanagawa Univ.) IT2017-121 ISEC2017-109 WBS2017-102 |
Digital watermarking is a technique of embedding information in an original image without being noticed or disclosed to ... [more] | IT2017-121 ISEC2017-109 WBS2017-102 pp.103-106 |
| IBISML | 2018-03-06 10:50 |
Fukuoka | Nishijin Plaza, Kyushu University | The improving method of Singular Bayesian information criterion by analyzing learning coefficients Sayaka Suzuki, Souta Shina, Miki Aoyagi (Nihon Univ.) IBISML2017-100 |
Real data associated with genetic analysis, data mining, image or speech recognition, artificial intelligence, the cont... [more] |
IBISML2017-100 pp.71-76 |
| ICSS | 2017-11-21 10:55 |
Oita | Beppu International Convention Center | Subspecies Classification for Malwares based on Visualization of Dynamic Analysis result Taiki Gouda, Korehito Kashiki, Keisuke Furumoto, Masakatu Morii (Kobe Univ.) ICSS2017-45 |
Along with the activation of malware infection activity, the appearance frequency of new subspecies also tends to increa... [more] | ICSS2017-45 pp.41-45 |
| PRMU, IPSJ-CVIM, MVE [detail] | 2016-01-22 15:35 |
Osaka | A Note on the Computational Complexity Reduction Method of the Optimal Prediction under Bayes Criterion in Semi-Supervised Learning Yuto Nakano, Shota Saito, Toshiyasu Matsushima (Waseda Univ.) PRMU2015-130 MVE2015-52 |
In this paper, we deal with a prediction problem of the semi-supervised learning based on the statistical decision theor... [more] | PRMU2015-130 MVE2015-52 pp.275-280 |
|
| IT | 2015-07-13 15:15 |
Tokyo | Tokyo Institute of Technology | Design and Analysis of MDL Estimators for Supervised Learning Jun'ichi Takeuchi, Masanori Kawakita (Kyushu Univ.) IT2015-26 |
Barron and Coveys theory to evaluate risk bounds for the MDL estimators (1991) is basically for unsupervised learning... [more] |
IT2015-26 pp.53-58 |
| IBISML | 2014-11-17 17:00 |
Aichi | Nagoya Univ. | [Poster Presentation]
Training Algorithm for Restricted Boltzmann Machines Using Auxiliary Function Approach Norihiro Takamune (Univ. of Tokyo), Hirokazu Kameoka (Univ. of Tokyo/NTT) IBISML2014-56 |
Layerwise pre-training is one of important elements for deep learning, and Restricted Boltzmann Machines (RBMs) is popul... [more] | IBISML2014-56 pp.161-168 |
| ET | 2014-10-18 16:05 |
Ishikawa | Kanazawa Univ. (Kakuma Campus) | Basic Consideration for Generating Auxiliary Problems in Algorithm Learning Minori Fuwa, Mizue Kayama, Hisayoshi Kunimune, Masami Hashimoto, Makoto Otani (Shinshu Univ.) ET2014-49 |
The purpose of this study is to generate auxiliary problems in algorithm learning for novices. Our domain is an algorit... [more] | ET2014-49 pp.61-66 |
| TL | 2013-10-19 13:15 |
Tokyo | WASEDA Univ. | Exploring linguistic features for the automated assessment of L2 spoken English Yuichiro Kobayashi (JSPS), Mariko Abe (Chuo Univ.) TL2013-41 |
The present study aims to automatically evaluate second language (L2) spoken English, and to identify linguistic feature... [more] | TL2013-41 pp.1-6 |
| NC, MBE (Joint) |
2012-12-12 10:40 |
Aichi | Toyohashi University of Technology | A numerical derivation of learning coefficient in radial basis function network Satoru Tokuda, Kenji Nagata, Masato Okada (Univ. of Tokyo) NC2012-78 |
Radial basis function (RBF) network is a regression model which regresses input-output data by radial basis functions su... [more] | NC2012-78 pp.25-30 |
| PRMU, IBISML, IPSJ-CVIM (Joint) [detail] |
2012-09-02 10:00 |
Tokyo | Information theoretic clustering using competitive learning
-- Comparison of criterion functions and algorithms for document clustering -- Toshio Uchiyama (NTT) PRMU2012-33 IBISML2012-16 |
Information-theoretic clustering (ITC) finds clusters based on the similarity of the distributions of features. An ITC a... [more] | PRMU2012-33 IBISML2012-16 pp.23-30 |
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| IBISML | 2012-03-12 15:05 |
Tokyo | The Institute of Statistical Mathematics | Model Selection of Indirect Value Function Estimation Masahiro Kohjima (Tokyo Tech) IBISML2011-93 |
Reinforcement learning is a method to obtain a policy which maximizes expected return and is applied to wide range of re... [more] | IBISML2011-93 pp.43-48 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2011-09-05 11:30 |
Hokkaido | Learning of Kernel Classfier based on General Loss Minimization Masato Ishii, Atsushi Sato (NEC) PRMU2011-61 IBISML2011-20 |
This paper presents a new method for learning kernel classifiers. First, we formulate a novel learning scheme called ``G... [more] | PRMU2011-61 IBISML2011-20 pp.23-30 |
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| PRMU, IBISML, IPSJ-CVIM [detail] | 2011-09-06 14:50 |
Hokkaido | A Method for Multiple Instance Learning Using Sparse Kernel Machines Kazuhisa Nagashima, Masato Inoue (Waseda Univ.) PRMU2011-77 IBISML2011-36 |
Multiple Instance Learning problem (MIL) is roughly one of the classification problems. In generally classification pr... [more] |
PRMU2011-77 IBISML2011-36 pp.159-163 |
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