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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| PRMU, MVE, VRSJ-SIG-MR, IPSJ-CVIM | 2024-01-26 15:34 |
Kanagawa | Keio Univ. (Hiyoshi Campus) | Comparison of Imbalanced Data Handling Techniques in Emotion Estimation of Expressway Service Area Workers using Stacking Ensemble Learners for Complex Decision Boundaries Akihiro Sato, Satoki Ogiso, Ryosuke Ichikari, Takeshi Kurata (AIST)  PRMU2023-47 |
Estimating emotions of workers is promising to promote health and productivity management, while it has difficulty in c... [more] | PRMU2023-47 pp.40-45 |
| CCS | 2023-03-26 11:35 |
Hokkaido | RUSUTSU RESORT | A Study on Hardware Architectures of Ensemble Kalman Filters towards High-Speed and Memory-Efficient Online Learning for Reservoir Computing Kota Tamada, Yuki Abe, Kose Yoshida, Tetsuya Asai (Hokkaido Univ) CCS2022-70 |
The objective of this study was to develop a hardware architecture for an ensemble Kalman filter in reservoir computing.... [more] | CCS2022-70 pp.42-47 |
| MBE | 2021-05-28 13:55 |
Online | Online | Emotional Estimation by Micro-expression Using Ensemble Learning Koki Kato, Hironobu Takano (Toyama Pref. Univ.), Masahiro Saiko, Masahiro Kubo, Hitoshi Imaoka (NEC) MBE2021-2 |
Bad news, such as cancer notifications from doctors, has a big impact on patients. The patient does not remember the con... [more] | MBE2021-2 pp.2-5 |
| 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 |
| 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 |
| 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 |
| NLP | 2005-11-18 13:25 |
Fukuoka | Kyushu Institute of Technology | Ensemble Self-Generating Neural Networks for Chaotic Time Series Prediction Masaki Nakahara, Hirotaka Inoue (KNCT) |
In this paper,we present a performanse characteristic of self-generating neural networks(SGNNs) applied to time series ... [more] |
NLP2005-63 pp.7-12 |
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