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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 114  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
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
RISING
(2nd)
2019-11-26
10:30
Tokyo Fukutake Learning Theater, Hongo Campus, Univ. Tokyo [Poster Presentation] Prioritized Transmission of Mobile IoT Data Using Machine Learning Models
Yuichi Inagaki, Ryoichi Shinkuma, Takehiro Sato, Oki Eiji (Kyoto Univ.)
Predicting real-time spatial information from data collected by the mobile Internet of Things (IoT) devices is one solut... [more]
RISING
(2nd)
2019-11-27
13:55
Tokyo Fukutake Learning Theater, Hongo Campus, Univ. Tokyo [Poster Presentation] Modeling of Utility Function for Real-time Prediction of Spatial Information Using Machine Learning
Keiichiro Sato, Ryoichi Shinkuma, Takehiro Sato, Eiji Oki (Kyoto Univ.), Takahiro Iwai, Takeo Onishi, Takahiro Nobukiyo, Dai Kanetomo, Kozo satoda (System platform Research Labs, NEC Corporation)
Real-time prediction of spatial information has attracted a lot of attention. Machine learning enables us to provide rea... [more]
 Results 1 - 20 of 114  /  [Next]  
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