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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IBISML | 2024-12-21 11:30 |
Hokkaido | Lecture room 1 (D101), Graduate School of Environmental Science (Primary: On-site, Secondary: Online) |
Assessing predictive performance and model interpretability through SHAP-based feature selection Ryota Hashiura, Akihiro Omori, Naoto Nakano (Meiji Univ.)  IBISML2024-47 |
(To be available after the conference date) [more] | IBISML2024-47 pp.108-115 |
| NC, MBE (Joint) |
2024-09-27 13:50 |
Miyagi | Tohoku Univ. (Primary: On-site, Secondary: Online) |
Brain-information based grouping for preference estimation in video delivery Taisei Oishi, Ryoichi Shinkuma (SIT), Naoya Maeda (NTT DATA), Satoshi Nishida (NICT) NC2024-33 |
In the rapidly growing market of video delivery and advertising, leveraging brain information has become a focus for the... [more] | NC2024-33 pp.7-10 |
| IBISML | 2023-12-21 15:25 |
Tokyo | National Institute of Informatics (Primary: On-site, Secondary: Online) |
A linear time approximation of Wasserstein distance with word embedding selection Sho Otao (Kyoto Univ.), Makoto Yamada (OIST) IBISML2023-38 |
Wasserstein distance, which can be computed by solving the optimal transport problem, is a powerful method for measuring... [more] | IBISML2023-38 pp.50-57 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-03 16:25 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
Fast Identification of Possible Model Parameter Update for Low-Rank Update of Training Data Hiroyuki Hanada, Noriaki Hashimoto (RIKEN), Kouichi Taji, Ichiro Takeuchi (Nagoya Univ.) PRMU2022-123 IBISML2022-130 |
Machine learning methods often require re-training the training dataset with low-rank modifications (small number of ins... [more] | PRMU2022-123 IBISML2022-130 pp.347-354 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2022-06-28 13:30 |
Okinawa | (Primary: On-site, Secondary: Online) |
Joint-Conditional Mutual Information Based Feature Subset Selection for Remotely Sensed Hyperspectral Image Classification U A Md Ehsan Ali, Keisuke Kameyama (Univ. Tsukuba) NC2022-16 IBISML2022-16 |
Hundreds of contiguous bands of remotely sensed hyperspectral image (HSI) capture the spectral signatures of observed ob... [more] | NC2022-16 IBISML2022-16 pp.115-122 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2022-06-28 13:55 |
Okinawa | (Primary: On-site, Secondary: Online) |
Feature selection in prediction model by LiNGAM Taiyu Sumida, Takashi Takekawa (Kogakuin Univ.) NC2022-17 IBISML2022-17 |
To improve the accuracy of machine learning models, it is important to perform feature engineering based on the features... [more] | NC2022-17 IBISML2022-17 pp.123-128 |
| SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] | 2022-05-20 15:20 |
Kumamoto | Kumamoto University Kurokami Campus (Primary: On-site, Secondary: Online) |
Person Verification Based on Finger-Writing of a Simple Symbol on a Smartphone
-- Improvement of polar transformation and effect of fusing uncorrelated features -- Isao Nakanishi, Kazuki Matsuura, Yohei Masegi, Takahiro Horiuchi (Tottori Univ.) SIP2022-26 BioX2022-26 IE2022-26 MI2022-26 |
We have studied to authenticate users based on their finger writing. Users are asked to draw or write a simple symbol ... [more] |
SIP2022-26 BioX2022-26 IE2022-26 MI2022-26 pp.132-137 |
| RCS, SR, SRW (Joint) |
2022-03-02 10:50 |
Online | Online | Pilot Pattern Design Method using Autoencoder for CDL Channels Yuta Yamada, Tomoaki Ohtsuki (Keio Univ.) RCS2021-253 |
In the pilot-based channel estimations, a large number of pilot signals enable an improvement in the channel estimation ... [more] | RCS2021-253 pp.13-18 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2021-06-28 16:10 |
Online | Online | More Powerful and General Selective Inference for Stepwise Feature Selection using Homotopy Method Kazuya Sugiyama (Nitech), Vo Nguyen Le Duy, Ichiro Takeuchi (Nitech/RIKEN) NC2021-8 IBISML2021-8 |
Conditional selective inference (SI) has been actively studied as a new statistical inference framework for data-driven ... [more] | NC2021-8 IBISML2021-8 pp.55-61 |
| IA, ICSS | 2021-06-22 09:00 |
Online | Online | Feature analysis of phishing website and phishing detection based on machine learning algorithms Yi Wei, Yuji Sekiya (Todai) IA2021-9 ICSS2021-9 |
Phishing is a kind of cybercrime that uses disguised websites to trick people into providing personally sensitive inform... [more] | IA2021-9 ICSS2021-9 pp.44-49 |
| KBSE, SWIM | 2021-05-22 10:30 |
Online | Online | Feature Selection for Avoiding Overfitting of Software Defect Prediction Yuta Nagai, Ryuichi Takahashi (Ibaraki Univ.) KBSE2021-7 SWIM2021-7 |
Software defect prediction by machine learning is important for software development, and there is much research on how ... [more] | KBSE2021-7 SWIM2021-7 pp.37-43 |
| IN, NS (Joint) |
2021-03-05 11:00 |
Online | Online | A Model Selection Optimization Method for Distributed Machine Learning with Feature Model Combination Ryuichi Mochizuki, Takeshi Tsuchiya, Hiroo Hirose, Tetsuyasu Yamada (SUS) IN2020-83 |
This study clarifies the optimal feature model selection method in data analysis under the environment where the feature... [more] | IN2020-83 pp.172-177 |
| BioX, CNR | 2021-03-02 09:30 |
Online | Online | Construction and Evaluation of an Emotion Estimation Model Using EEG and Heart Rate Variability Indices Kei Suzuki, Ryota Matsubara, Midori Sugaya (Shibaura Inst. of Tech.) BioX2020-40 CNR2020-13 |
Urabe et al. have conducted research on human emotion estimation techniques. They constructed an emotion estimation mode... [more] | BioX2020-40 CNR2020-13 pp.1-6 |
| IBISML | 2021-03-02 10:50 |
Online | Online | Kernel tensor decomposition based unsupervised feature extraction
-- Applications to bioinformatics -- Y-h. Taguchi (Chuo Univ.) IBISML2020-36 |
A lot of research has been done on the so-called textit{large p small n} problem, where the number of samples is small c... [more] | IBISML2020-36 pp.16-23 |
| AP | 2020-12-17 13:50 |
Online | Online | A Study on Urban Structure Map Extraction for Radio Propagation Prediction using XGBoost Tatsuya Nagao, Takahiro Hayashi (KDDI Research) AP2020-98 |
Recently, the rapid increase in mobile data traffic and the diversification of wireless communication services have led ... [more] | AP2020-98 pp.13-17 |
| IA | 2020-10-01 11:15 |
Online | Online | Malicious URLs Detection Using an Integrated AI Framework Bo-Xiang Wang, Ren-Feng Deng, Yi-Wei Ma, Jiann-Liang Chen (NTUST) IA2020-1 |
Malicious attacks on computer networks are quite common, and the internet attacks are even more widespread, such as Malv... [more] | IA2020-1 pp.1-5 |
| SP, EA, SIP | 2020-03-02 13:00 |
Okinawa | Okinawa Industry Support Center (Cancelled but technical report was issued) |
[Poster Presentation]
A Robust Approach to Jointly-Sparse Signal Recovery Based on Minimax Concave Loss Function Kyohei Suzuki, Masahiro Yukawa (Keio Univ.) EA2019-122 SIP2019-124 SP2019-71 |
We propose a robust approach to recovering the jointly sparse signals in the presence of outliers. The proposed approach... [more] | EA2019-122 SIP2019-124 SP2019-71 pp.123-128 |
| IBISML | 2020-01-09 16:45 |
Tokyo | ISM | Application of tensor decomposition based unsupervised feature extraction to single cell RNA-seq analysis Y-h. Taguchi (Chuo Univ.) IBISML2019-26 |
Cannonical correlation analysis (CCA) is known to integrate two matrices, each of which have elements, $x_{ij} in mathbb... [more] | IBISML2019-26 pp.55-59 |
| NLC, IPSJ-NL, SP, IPSJ-SLP (Joint) [detail] |
2019-12-04 11:15 |
Tokyo | NHK Science & Technology Research Labs. | Shiori Koga (Kyushu Univ.), Tsunenori Mine (kyushu Univ.), Sachio Hirokawa (Kyushu Univ.) NLC2019-31 |
Among RNNs, especially LSTM is capable of long-term memory and can be expected to acquire information including better c... [more] | NLC2019-31 pp.13-18 |
| AI | 2019-11-28 15:15 |
Fukuoka | Effectiveness of feature selection as pre-processing of LSTM using W2V Shiori Koga, Tsunenori Mine, Sachio Hirokawa (Kyushu Univ.) AI2019-34 |
Among RNNs, especially LSTM is capable of long-term memory, and can be expected to acquire information including better ... [more] | AI2019-34 pp.25-30 |
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