| IEICE Technical Committee Submission System Conference Schedule |
Online Proceedings [Sign in] Tech. Rep. Archives |
All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| ET | 2017-11-11 16:20 |
Tokyo | Sophia Univ. | Utilizing Public Data to Learning System for Development of Skills to Interpret and Express Statistics Katsumi Yoshine (Nanzan Univ.)  ET2017-65 |
In the current Course of Study for senior high schools, statistics are required in Mathematics. In the next Course of St... [more] | ET2017-65 pp.65-68 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | Statistical Mechanical Analysis of Learning with Two-Layer Perceptron with Multiple Output Units
-- Reconsidering Plateau Phenomenon -- Yuki Yoshida (UTokyo), Ryo Karakida (AIST), Masato Okada (UTokyo/AIST/RIKEN BSI), Shun-ichi Amari (RIKEN BSI) IBISML2017-83 |
The plateau phenomenon --- stopping decrease of error in the middle of learning --- is problematic. Since Amari et al. ... [more] |
IBISML2017-83 pp.347-354 |
| SP, IPSJ-SLP, NLC, IPSJ-NL (Joint) [detail] |
2016-12-20 16:40 |
Tokyo | NTT Musashino R&D | Generative Adversarial Network-based Postfiltering for Statistical Parametric Speech Synthesis Takuhiro Kaneko, Hirokazu Kameoka, Nobukatsu Hojo, Yusuke Ijima, Kaoru Hiramatsu, Kunio Kashino (NTT) SP2016-61 |
In the field of speech synthesis, statistical parametric speech synthesis has been widely used due to the flexibility an... [more] | SP2016-61 pp.89-94 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Statistical Mechanical Analysis of Fast Online Learning with Weight Normalization Yuki Yoshida, Ryo Karakida, Masato Okada (UTokyo), Shun-ichi Amari (RIKEN) IBISML2016-60 |
Weight normalization (WN), a newly developed optimization algorithm for neural networks by Salimans & Kingma(2016), fact... [more] | IBISML2016-60 pp.101-108 |
| NC, NLP (Joint) |
2016-01-29 15:25 |
Fukuoka | Kyushu Institute of Technology | Statistical Mechanics of Perceptron Learning with Noisy Teacher Arata Honda, Kazushi Ikeda (NAIST) NC2015-65 |
Learning curves of simple perceptron were derived here. They have been analyzed for half a century and the learning curv... [more] | NC2015-65 pp.45-48 |
| NC, NLP (Joint) |
2016-01-29 15:50 |
Fukuoka | Kyushu Institute of Technology | Node-perturbation Learning for Soft-committee machine Kazuyuki Hara (Nihon Univ.), Kentaro Katahira (Nagoya Univ.) NC2015-66 |
Node perturbation learning is a stochastic gradient descent method for neural networks. It estimates the gradient of the... [more] | NC2015-66 pp.49-54 |
| SR, SRW (Joint) |
2015-10-27 09:30 |
Tokyo | KKE | [Technology Exhibit]
Hardware Demonstration of Wi-Fi Fingerprint based Millimeter Wave Beamforming Ehab Mahmoud Mohamed (Osaka Univ.), Gia Khanh Tran, Kei Sakaguchi (Tokyo Tech), Seiichi Sampei (Osaka Univ.) SR2015-62 SRW2015-43 |
Millimeter wave (mmWave), especially the 60 GHz band, has been receiving much attention as a key enabler for the 5G cell... [more] | SR2015-62 SRW2015-43 pp.59-60 |
| ET | 2015-03-14 16:05 |
Tokushima | Shikoku Univ. Plaza | Proposal of a Design Framework for Gaming Instructional Materials to Promote Utilization of Statistical Ways of Viewing and Thinking Anna Kokubo, Toshiki Matsuda (Tokyo Tech) ET2014-106 |
Basic statistics are required to be learned both in mathematics at lower secondary school level and upper secondary sch... [more] | ET2014-106 pp.119-124 |
| RCS, SR, SRW (Joint) |
2015-03-06 09:25 |
Tokyo | Tokyo Institute of Technology | WiFi Assisted Dual-band MAC Protocol for Coordinated Beamforming among WiGig APs in Indoor Environment Ehab Mahmoud Mohamed, Hideyuki Kusano, Kei Sakaguchi, Shinichi Miyamoto, Seiichi Sampei (Osaka Univ.) SR2014-138 |
Wireless Gigabit (WiGig) Access Points (APs) using 60 GHz unlicensed frequency band are considered as a key enabler for ... [more] | SR2014-138 pp.163-168 |
| SIP | 2014-08-28 15:30 |
Osaka | Ritsumeikan Univ. (Osaka Umeda Campus) | [Tutorial Lecture]
Statistical-Mechanical Approach to Signal Processing Seiji Miyoshi (Kansai Univ.) SIP2014-78 |
Statistical mechanics, which is a branch of physics, was constructed with the aim of understanding the macroscopic prope... [more] | SIP2014-78 p.29 |
| SP, IPSJ-SLP (Joint) |
2014-07-25 11:20 |
Iwate | Hotel Hanamaki | [Invited Talk]
Speech research as a problem of statistical machine learning Yoshihiko Nankaku (Nagoya Inst. of Tech.) SP2014-67 |
This paper discusses the similarities and differences between speech recognition and speech synthesis techniques from a ... [more] | SP2014-67 pp.25-30 |
| KBSE | 2014-01-27 11:10 |
Tokyo | Kikai-Shinko-Kaikan Bldg | A Proposal on Answer Sentence Generation Method for Question Answering Using Statistical Machine Translation Tessei Yamada, Tatsuya Arakawa (Gunma NCT) KBSE2013-66 |
Question answering generally generates the answer to the question by extracting the named entity from the sentences cont... [more] | KBSE2013-66 pp.7-12 |
| IBISML | 2013-11-13 15:45 |
Tokyo | Tokyo Institute of Technology, Kuramae-Kaikan | [Poster Presentation]
Statistical-mechanical analysis of active noise control Rei Fujiwara, Yoshinobu Kajikawa, Seiji Miyoshi (Kansai Univ.) IBISML2013-65 |
We analyze the dynamical behaviors (learning curves) of the active noise control using a statistical-method. Cross-co... [more] |
IBISML2013-65 pp.219-224 |
| NC, MBE (Joint) |
2013-07-19 14:30 |
Tokushima | The University of Tokushima | Statistical Mechanics of node-perturbation Learning using two independent noises Kazuyuki Hara (Nihon Univ.), Kentaro Katahira, Masato Okada (Univ. of Tokyo) NC2013-17 |
Node perturbation learning is a stochastic gradient descent method for neural networks. It estimates the gradient by com... [more] | NC2013-17 pp.13-18 |
| CQ, CS (Joint) |
2013-04-18 16:15 |
Niigata | Sado Island Development Center | [Special Invited Talk]
Discovering Latent Structure by Statistical Machine Learning Methods Takeshi Yamada (NTT) CS2013-4 CQ2013-5 |
Recently, Big-data analysis attracts a lot of attentions. In such data analysis, it is important to discover and extract... [more] | CS2013-4 CQ2013-5 pp.19-24(CS), pp.25-30(CQ) |
| ET | 2013-03-29 16:20 |
Ehime | Interactive Learning Support System in cooperation with Teaching Materials Database for Exercise of Statistical Data Processing Katsumi Yoshine (Nanzan Univ.), Yukuo Isomoto (Nagoya Women's Univ.), Makoto Hasegawa (Junior College, Gifu Shotoku Gakuen Univ.) ET2012-115 |
To understand the current state of social and economic data and to contribute to policymaking and decision-making is the... [more] | ET2012-115 pp.189-194 |
|
| MI | 2013-01-24 17:05 |
Okinawa | Bunka Tenbusu Kan | Automated segmentation of the upper digestive tract from abdominal contrast-enhanced CT data using hierarchical statistical modeling of organ inter-relations Shunta Hirayama, Toshiyuki Okada, Masatoshi Hori, Noriyuki Tomiyama, Yoshinobu Sato (Osaka Univ.) MI2012-91 |
We have studied the automatic segmentation of multi-organ region from abdominal CT images ever. In previous work, we hav... [more] | MI2012-91 pp.149-154 |
| MBE, NC (Joint) |
2012-11-17 13:30 |
Miyagi | Tohoku University | Composite likelihood estimation for bipartite Boltzmann machines Takashi Asari, Muneki Yasuda, Yuji Waizumi, Kazuyuki Tanaka (Tohoku Univ.) NC2012-68 |
The recent development of information technology has enabled us to obtain and to storage huge information data. Because... [more] |
NC2012-68 pp.39-44 |
| IBISML | 2012-11-07 15:30 |
Tokyo | Bunkyo School Building, Tokyo Campus, Tsukuba Univ. | Regularization of Restricted Boltzmann Machine Learning through entropy minimization Taichi Kiwaki, Takaki Makino, Kazuyuki Aihara (Univ. Tokyo) IBISML2012-48 |
We propose a learning scheme for Restricted Boltzmann Machines (RBMs) that suppresses over-fitting, where the entropy of... [more] | IBISML2012-48 pp.103-106 |
| ET | 2012-06-23 13:40 |
Aichi | An Interactive Learning Support System for Improvement of the Ability for Explanation of Some Statistic
-- Aim to Assist in Writing a Statistical Analysis Report -- Katsumi Yoshine (Nanzan Univ.), Yukuo Isomoto (Nagoya Women's Univ.), Makoto Hasegawa (Junior College, Gifu Shotoku Gakuen Univ.) ET2012-14 |
In the introductory stage of a class of statistical data analysis, after the lecture of basic statistics, we will practi... [more] | ET2012-14 pp.13-18 |
|
[Return to Top Page]