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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| BioX, CNR | 2021-03-02 09:50 |
Online | Online | Improving an Accuracy of Personal Identification Using Ensemble Learning and Footsteps Yoshiki Goto, Akitoshi Itai (Chubu Univ.)  BioX2020-41 CNR2020-14 |
It is known that the footstep includes personal characteristics. We often recognize a person from walking footsteps in l... [more] | BioX2020-41 CNR2020-14 pp.7-11 |
| MSS, NLP (Joint) |
2020-03-09 15:45 |
Aichi | (Cancelled but technical report was issued) |
Two convolutional neural networks trained through Co-teaching perform a complementary role Toshikazu Samura, Katsumi Tadamura (Yamaguchi Univ.) NLP2019-123 |
Deep learning technology needs big labeled data without noisy labels to improve its performance. However, the costs for ... [more] | NLP2019-123 pp.61-64 |
| IE, IMQ, MVE, CQ (Joint) [detail] |
2020-03-06 11:10 |
Fukuoka | Kyushu Institute of Technology (Cancelled but technical report was issued) |
Investigation of diagnostic support system for portable chest X-ray images by two-step classification using ensemble learning Takahiro Dozono, Yuichiro Yoshimura, Kumiko Arai, Takaaki Nakada, Shigeto Oda, Toshiya Nakaguchi (Chiba Univ) IMQ2019-33 IE2019-115 MVE2019-54 |
Portable radiographs are used to monitor critically ill patients in intensive care units who have difficulty moving. How... [more] | IMQ2019-33 IE2019-115 MVE2019-54 pp.89-92 |
| IPSJ-SLDM, RECONF, VLD, CPSY, IPSJ-ARC [detail] | 2020-01-22 17:20 |
Kanagawa | Raiosha, Hiyoshi Campus, Keio University | Many Universal Convolution Cores for Ensemble Sparse Convolutional Neural Networks Ryosuke Kuramochi, Youki Sada, Masayuki Shimoda, Shimpei Sato, Hiroki Nakahara (Titech) VLD2019-65 CPSY2019-63 RECONF2019-55 |
A convolutional neural network (CNN) is one of the most successful neural networks and widely used for computer vision t... [more] | VLD2019-65 CPSY2019-63 RECONF2019-55 pp.67-72 |
| EMT, IEE-EMT | 2019-11-08 14:05 |
Saga | Hotel Syunkeiya | Localization of Cardiac Source with Lead Field Matrix by Ensemble Learning Tatsuhito Nakane, Takahiro Ito, Akimasa Hirata (NITech) EMT2019-69 |
An 12-lead electrocardiogram (ECG) were invented more than 100 years ago, and they are still used as an essential tool t... [more] | EMT2019-69 pp.213-216 |
| IT | 2019-07-25 14:50 |
Tokyo | NATULUCK-Iidabashi-Higashiguchi Ekimaeten | Bayes Optimal Classification on Decision Tree Model and Its Approximative Algorithm Using Ensemble Learning Nao Dobashi, Shota Saito, Toshiyasu Matsushima (Waseda Univ.) IT2019-17 |
In this paper we consider classification problem about discrete category $y$ regarding discrete variables $bm{x}$. Deci... [more] | IT2019-17 pp.11-16 |
| MoNA | 2019-01-17 10:05 |
Kyoto | T. B. D. | Study on Extraction of Important Data Based on Feature Selection Ensemble for Real-time Predictive Information Delivery Takumi Sakai, Ryoichi Shinkuma, Yuichi Inagaki, Takehiro Sato, Eiji Oki (Kyoto Univ) MoNA2018-67 |
Recently, the demands on the services that predict and deliver real-time spatial information, such as road-traffic volum... [more] | MoNA2018-67 pp.57-61 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Posterior mean approximation solution combining multiple image prior distributions in MR image reconstruction Nanako Kubota, Ken Harada (Waseda Univ.), Koji Fujimoto, Tomohisa Okada (Kyoto Univ.), Masato Inoue (Waseda Univ.) IBISML2018-47 |
In the MR image reconstruction, combining multiple image prior distributions is preferred to obtain better results, but ... [more] | IBISML2018-47 pp.23-28 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Revising the Algorithm of Ensenble Learning by an Index of Complementarity among Weak Learners Shota Utsumi, Keisuke Kameyama (Univ. of Tsukuba) IBISML2018-102 |
In ensemble learning, the performance of each weak learner and their acquisition of complementary functions affects the ... [more] | IBISML2018-102 pp.429-434 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2018-09-20 09:40 |
Fukuoka | Arrangement of Complementary Weak Learners using Weights Assigned to Data in Parallel Ensemble Learning Shota Utsumi, Keisuke Kameyama (Univ. of Tsukuba) PRMU2018-37 IBISML2018-14 |
The accuracy of each weak learner and acquisition of complementary functions among weak learners are important for impro... [more] | PRMU2018-37 IBISML2018-14 pp.9-15 |
|
| NLP, CCS | 2018-06-10 14:00 |
Kyoto | Kyoto Terrsa | Prediction of Foreign Exchange Rates by Price Quotations of Counterparty Banks
-- Using Collective Intelligence of Professional Views -- Takehiro Suzuki, Tomoya Suzuki (Ibaraki Univ.) NLP2018-47 CCS2018-20 |
In foreign-exchange (FX) dealing, FX brokers basically cancel out the orders from their customers to prevent the price f... [more] | NLP2018-47 CCS2018-20 pp.109-114 |
| MBE, NC, NLP (Joint) |
2018-01-26 13:00 |
Fukuoka | Kyushu Institute of Technology | A study on Detecting Event Related Potential P300 through Weighted Ensemble Learning using Convolutional Neural Network Takahiro Takeichi, Tomohiro Yoshikawa, Takeshi Furuhashi (Nagoya Univ.) NC2017-50 |
The event related potential P300 in the electroencephalogram (EEG) elicited by visual stimulus is used for P300 speller ... [more] | NC2017-50 pp.1-4 |
| MBE, NC (Joint) |
2017-12-16 11:20 |
Aichi | Nagoya University | A Study on Applying Convolutional Neural Network for Detecting Event Related Potential P300 Takahiro Takeichi, Tomohiro Yoshikawa, Takeshi Furuhashi (Nagoya Univ.) NC2017-42 |
The event related potential P300 in the electroencephalogram (EEG) elicited by visual stimulus is used for P300 speller ... [more] | NC2017-42 pp.13-16 |
| MBE, NC (Joint) |
2017-11-25 15:10 |
Miyagi | Tohoku University | Ensemble Learning with Feature Extraction for EEG Signal Discrimination using Source Separation Shuichi Nishino, Tomohiro Yoshikawa, Takeshi Furuhashi (Nagoya Univ.) NC2017-36 |
BCI allows a user to control external devices and to communicate with other people by measuring and discriminating EEG. ... [more] | NC2017-36 pp.49-52 |
| IA | 2017-11-15 13:50 |
Overseas | KMITL, Bangkok, Thailand | Machine Learning Approach for Phishing Detection in SDN Networking Yu-Hung Chen, Jiun-Yu Yang, Po-Chun Hou, Jiann-Liang Chen (National Taiwan University of Science & Technology) IA2017-30 |
People have become increasingly dependent on information technology since the emergence of the Internet. Therefore, many... [more] | IA2017-30 pp.1-6 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Application of Transfer Learning to Smallscale Data and Its Evaluation Using Open Datasets Arika Fukushima, Toru Yano, Shuuichiro Imahara, Hideyuki Aisu (Toshiba) IBISML2017-41 |
Large sample size of the training data is essential for high performance of prediction on machine learning. However, in... [more] |
IBISML2017-41 pp.47-53 |
| DE, IPSJ-DBS, IPSJ-IFAT | 2017-09-19 15:20 |
Tokyo | Ochanomizu University | Model Ensemble for Failure Event Detection using Multiple User Activity Data on the Web Motoyuki Oki (NTT Communications), Koh Takeuchi (NTT), Yukio Uematsu (NTT Communications) DE2017-20 |
Mobile network service providers aim to maintain stable operation and improve service performance using multiple user's ... [more] | DE2017-20 pp.123-128 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2017-09-15 10:00 |
Tokyo | Quantum-Inspired Regression Forest Zeke Xie, Issei Sato (UTokyo) PRMU2017-40 IBISML2017-12 |
We propose a Quantum-Inspired Subspace(QIS) Ensemble Method for generating feature ensembles based on feature selections... [more] | PRMU2017-40 IBISML2017-12 pp.7-17 |
|
| SIP, CAS, MSS, VLD | 2017-06-19 13:00 |
Niigata | Niigata University, Ikarashi Campus | [Invited Talk]
Composite Variables and Ensemble: Introduction to Forest Regression and Additive Models Ichigaku Takigawa (Hokkaido Univ.) CAS2017-8 VLD2017-11 SIP2017-32 MSS2017-8 |
Machine learning, supervised machine learning in particular, now becomes one of daily tools in signal processing such as... [more] | CAS2017-8 VLD2017-11 SIP2017-32 MSS2017-8 p.43 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | [Poster Presentation]
An ensemble learning for MR image reconstruction Yufu Kasahara, Masato Inoue (Waseda Univ), Kaori Togashi (Kyoto Univ) IBISML2016-58 |
In order to shorten the magnetic resonance (MR) imaging time, a lot of image reconstruction methods from a small number ... [more] | IBISML2016-58 pp.87-91 |
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