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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| PRMU | 2019-12-19 10:45 |
Oita |  PRMU2019-47 |
(To be available after the conference date) [more] | PRMU2019-47 pp.7-12 |
|
| 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 |
| 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 |
| PRMU, MVE, IPSJ-CVIM [detail] | 2018-01-18 17:40 |
Osaka | Takahiro Oga (Nagaoka Univ of Technology), Masaki Yano (Univ. of Tsukuba), Masaki Onishi (AIST) PRMU2017-128 MVE2017-49 |
(To be available after the conference date) [more] | PRMU2017-128 MVE2017-49 pp.135-140 |
|
| 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 |
| 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 |
| 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 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | [Poster Presentation]
Optimization Method of Deep Ensemble Learning using Hierarchical Clustering Natsuki Koda, Sumio Watanabe (Tokyo Tech) IBISML2016-70 |
The method which is used for prediction by combining many different learning machines generated by using same training d... [more] | IBISML2016-70 pp.171-176 |
| PRMU, IPSJ-CVIM, IBISML [detail] | 2016-09-05 18:30 |
Toyama | A Short Survey on Defect Detection for Inspection of Social Infrastructures Hiromitsu Fujii, Atsushi Yamashita, Hajime Asama (The Univ. of Tokyo) PRMU2016-74 IBISML2016-29 |
(To be available after the conference date) [more] | PRMU2016-74 IBISML2016-29 pp.163-166 |
|
| NC, NLP (Joint) |
2016-01-28 15:25 |
Fukuoka | Kyushu Institute of Technology | Validation of the Effects of Ensemble Learning for i-vector-based Speaker Identification
-- Bagging vs Random forest -- Shohei Sonoda, Masato Inoue (Waseda Univ) NC2015-58 |
Currently, most speaker identification methods have been performed by i-vectors which represent the features of unique s... [more] | NC2015-58 pp.13-16 |
| IBISML | 2015-11-27 14:00 |
Ibaraki | Epochal Tsukuba | [Poster Presentation]
Recursive Ensemble Land Cover Classification for Few Training Data and Many Class Yu Oya, Katsutoshi Kanamori, Hayato Ohwada (TUS) IBISML2015-77 |
Many global and environmental applications require land use and land cover information. A land cover classification is o... [more] | IBISML2015-77 pp.183-188 |
| IT | 2014-07-17 10:10 |
Hyogo | Kobe University | Distance Metric Learning with Low Computational Complexity based on Ensemble of Low-dimensional Matrixes Hiroshi Saito, Fumihiro Yamazaki, Kenta Mikawa, Masayuki Goto (Waseda Univ.) IT2014-12 |
The distance metric learning is the approach which enables to acquire a good metric for automatic data classification. I... [more] | IT2014-12 pp.7-12 |
| MI | 2014-01-26 10:15 |
Okinawa | Bunka Tenbusu Kan | Improvement of choroid segmentation from a macular optical coherence tomography volume based on graph cuts Kazuki Inagaki, Akinobu Shimizu (Tokyo Univ. of Agriculture and Tech.), Yoshiaki Yasuno (Univ. of Tsukuba), Yasushi Ikuno (Osaka Univ.) MI2013-57 |
(To be available after the conference date) [more] | MI2013-57 pp.7-12 |
| MI | 2014-01-26 13:30 |
Okinawa | Bunka Tenbusu Kan | Multi-organ localizations on a large number of CT images by using machine learning and its performance evaluations Shoichi Morita, Xiangrong Zhou, Huayue Chen, Takeshi Hara (Gifu Univ.), Huiyan Jiang (NEU), Ryujiro Yokoyama (Gifu Univ.), Masayuki Kanematsu (Gifu Univ. Hospital), Hiroaki Hoshi, Hiroshi Fujita (Gifu Univ.) MI2013-62 |
In this study, we propose an approach to accomplish general localization of the different inner organ regions on 3D CT s... [more] | MI2013-62 pp.37-41 |
| SIS | 2013-12-12 15:00 |
Tottori | Torigin Bunka Kaikan (Tottori) | Effects of input patterns including noise on generalization of neural network systems with ensemble learning Akihiro Tanaka, Satoru Kishida (Tottori Univ.) SIS2013-40 |
We produced input patterns including noise and investigated the effect of them on the generalization of neural network s... [more] | SIS2013-40 pp.71-74 |
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