Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
IBISML, NC, IPSJ-BIO, IPSJ-MPS [detail] |
2024-06-20 15:50 |
Okinawa |
OIST |
Distributionally Robust Safe Sample Screening and Its Application to Infinite-width Deep Neural Networks Tatsuya Aoyama (Nagoya Univ.), Hiroyuki Hanada (RIKEN), Satoshi Akahane, Yoshito Okura, Tomonari Tanaka (Nagoya Univ.), Yu Inatsu (NITech), Noriaki Hashimoto (RIKEN), Taro Murayama, Lee Hanju, Shinya Kojima (DENSO), Ichiro Takeuchi (Nagoya Univ.) |
(To be available after the conference date) [more] |
|
HIP, ITE-HI, VRPSY, ASJ-H [detail] |
2024-02-23 16:20 |
Okinawa |
|
SVMを用いた角度と距離特徴に基づく人の行動認識に関する研究 Cho Nilar Phyo, Thi Thi Zin, Pyke Tin (Univ. of Miyazaki), Hiromitsu Hama (Osaka City Univ.) HIP2023-112 |
Human action recognition is the important research area in computer vision research area and popular due to its enormous... [more] |
HIP2023-112 pp.95-96 |
ITS, IE, ITE-MMS, ITE-ME, ITE-AIT [detail] |
2024-02-20 13:45 |
Hokkaido |
Hokkaido Univ. |
Fine-tuning Image Classification Model for Diagnosis of Autism Spectrum Disorder Using EEG Data Hiroto Kawahara, Takuya Kitamura (NITT) ITS2023-70 IE2023-59 |
In this paper, we propose and evaluate a classification model generated by fine tuning a pre-trained image classificatio... [more] |
ITS2023-70 IE2023-59 pp.130-134 |
ITS, IE, ITE-MMS, ITE-ME, ITE-AIT [detail] |
2024-02-20 14:00 |
Hokkaido |
Hokkaido Univ. |
Fine-tuning Model for Diagnosis of Autism Spectrum Disorder Using fMRI Data Sae Yoshihara, Takuya Kitamura (NITT) ITS2023-71 IE2023-60 |
In this paper, we propose and evaluate novel classification models generated by fine-tuning a pre-trained image classifi... [more] |
ITS2023-71 IE2023-60 pp.135-140 |
MSS, SS |
2023-01-11 15:50 |
Osaka |
(Primary: On-site, Secondary: Online) |
Improvement of Composite SVM in HSI Classification Tamura Akito, Kitamura Takuya (NIT) MSS2022-61 SS2022-46 |
In this paper, we propose an improved method of composite support vector machines for hyper-spectral image classificatio... [more] |
MSS2022-61 SS2022-46 pp.96-100 |
SIS, ITE-BCT |
2021-10-07 14:25 |
Online |
Online |
Block-wise Transformation with Secret Key for Adversary Robust Defence of SVM model Ryota Iijima, MaungMaung AprilPyone, Hitoshi Kiya (TMU) SIS2021-13 |
In this paper, we propose a method for implementing support vector machine (SVM) models that are robust against adversar... [more] |
SIS2021-13 pp.17-22 |
CCS, NLP |
2020-06-05 15:25 |
Online |
Online |
Early detection and prevention of combustion oscillations using a symbolic dynamics/machine learning-based approach Kazuki Asami, Shinga Masuda, Hiroshi Gotoda (TUS) NLP2020-19 CCS2020-9 |
We have conducted an experimental study on an early detection and prevention of combustion oscillations in a laboratory-... [more] |
NLP2020-19 CCS2020-9 pp.41-44 |
NS, IN (Joint) |
2020-03-06 11:00 |
Okinawa |
Royal Hotel Okinawa Zanpa-Misaki (Cancelled but technical report was issued) |
To Evaluate the Benefits of Compound Words for Determining if a Test Case is Necessary using Support Vector Machine Satoshi Sunaga, Kazuhiro Kikuma (NTT), Shota Inokoshi, Koki Sato, Kiyoshi Ueda (Nihon Univ.) NS2019-226 |
Communication software used for Next Generation Network (NGN) etc.
requires high reliability and therefore adopts many... [more] |
NS2019-226 pp.271-276 |
IN, NS (Joint) |
2019-03-05 15:40 |
Okinawa |
Okinawa Convention Center |
Main Part-of-speech Selection for Verification Necessity Determination by Support Vector Machine Satoshi Sunaga, Koji Hoshino, Kazuhiro Kikuma (NTT), Koki Jimbo, Koki Satoh, Kiyoshi Ueda (NIHON Univ.) NS2018-288 |
Communication software used for Next Generation Network (NGN) etc.
requires high reliability and therefore adopts many... [more] |
NS2018-288 pp.545-550 |
HWS, VLD |
2019-02-28 13:30 |
Okinawa |
Okinawa Ken Seinen Kaikan |
Selection of Gaussian Mixture Reduction Methods Using Machine Learning Haruki Kazama, Shuji Tsukiyama (Chuo Univ.) VLD2018-113 HWS2018-76 |
Gaussian mixture model is a useful distribution for statistical methods such as statistical static timing analysis, but ... [more] |
VLD2018-113 HWS2018-76 pp.121-126 |
CQ, ICM, NS, NV (Joint) |
2018-11-16 12:20 |
Ishikawa |
|
A Method of Verification Necessity Determination using Support Vector Machine Satoshi Sunaga, Koji Hoshino, Kazuhiro Kikuma (NTT), Koki Jimbo, Koki Satoh, Kiyoshi Ueda (NIHON Univ.) NS2018-147 |
Since communication software typified by Next Generation Network (NGN)
is required to have high reliability, it incorp... [more] |
NS2018-147 pp.99-104 |
CPSY, DC, IPSJ-ARC [detail] |
2018-06-15 14:40 |
Yamagata |
Takamiya Rurikura Resort |
A Note on Ransomeware Detection using Support Vector Machines Yuuki Takeuchi, Kazuya Sakai, Satoshi Fukumoto (Tokyo Metropolitan Univ.) CPSY2018-10 DC2018-10 |
Recently, the damage of Ransomware has spread around the world.Ransomware is malware that requires users to pay money as... [more] |
CPSY2018-10 DC2018-10 pp.131-136 |
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] |
2018-06-13 16:15 |
Okinawa |
Okinawa Institute of Science and Technology |
Enumeration of Distinct Support Vectors for Model Selection Kentaro Kanamori (Hokaido Univ.), Satoshi Hara (Osaka Univ.), Masakazu Ishihata (NTT), Hiroki Arimura (Hokaido Univ.) IBISML2018-12 |
In ordinary machine learning problems, the learning algorithm outputs a single model that optimizes its learning objecti... [more] |
IBISML2018-12 pp.81-88 |
SP, IPSJ-SLP, NLC, IPSJ-NL (Joint) [detail] |
2016-12-22 10:00 |
Tokyo |
NTT Musashino R&D |
An Estimation of Kaomoji's Original Form using Classifiers Noriyuki Okumura (NITAC) NLC2016-37 |
This paper describes an estimation method of Kaomoji’s original forms using classifiers. We select features to use class... [more] |
NLC2016-37 pp.93-96 |
AI |
2016-12-09 10:55 |
Oita |
|
Play Estimation Based on Player's View in American Football Hana Miwa, Yasuhiko Kitamura (Kwansei Gakuin Univ.) AI2016-15 |
Play estimation in previous research has been based on the movement of all the players in the field, which estimates a p... [more] |
AI2016-15 pp.17-22 |
PRMU, IPSJ-CVIM, IBISML [detail] |
2016-09-06 11:15 |
Toyama |
|
Hyper-parameter Optimization with Derivative-free Method Yoshihiko Ozaki, Masaki Yano (Univ. Tsukuba/AIST), Masaki Onishi (AIST), Takahito Kuno (Univ. Tsukuba) PRMU2016-84 IBISML2016-39 |
In machine learning methods, an appropriate hyper-parameter tuning is really important for classifiers to perform its be... [more] |
PRMU2016-84 IBISML2016-39 pp.227-232 |
ICSS, IPSJ-SPT |
2016-03-04 14:30 |
Kyoto |
Academic Center for Computing and Media Studies, Kyoto University |
An Autonomous DDoS Backscatter Detection System from Darknet Traffic Yuki Ukawa, Jun Kitazono, Seiichi Ozawa (Kobe Univ.), Tao Ban, Junji Nakazato (NICT), Jumpei Shimamura (clwit) ICSS2015-67 |
This paper proposes an autonomous DDoS backscatter detection system from UDP darknet traffic. To identify DDoS backscatt... [more] |
ICSS2015-67 pp.123-128 |
RCS, CCS, SR, SRW (Joint) |
2016-03-04 16:20 |
Tokyo |
Tokyo Institute of Technology |
A Study on Location Estimation Method by Wi-SUN Using Machine Learning Hiroshi Sakamoto, Hiroyuki Yasuda, Thong Huynh, Kaori Kuroda (Tokyo Univ. of Science), Yozo Shoji (NICT), Mikio Hasegawa (Tokyo Univ. of Science) CCS2015-78 |
Wi-SUN is a wireless communication standard that has been developed as communication scheme for smart meter to record in... [more] |
CCS2015-78 pp.63-66 |
IBISML |
2014-11-17 17:00 |
Aichi |
Nagoya Univ. |
[Poster Presentation]
Efficient leave-one-out cross-validation for L2-regularized classifier Shota Okumura, Yoshiki Suzuki, Kohei Ogawa, Yuki Shinmura, Ichiro Takeuchi (NIT) IBISML2014-44 |
Leave-one-out cross-validation (LOOCV) is a useful tool
for estimating generalization performances of
various machine ... [more] |
IBISML2014-44 pp.73-80 |
PRMU, CNR |
2014-02-13 11:00 |
Fukuoka |
|
Face recognition using Support vector machine Shintaro Obayashi, Shota Funaki, Yuki Tsukagoshi, Takuya Kitamura (TNCT) PRMU2013-126 CNR2013-34 |
In this paper, we demonstrate the effectiveness of support vector machines (SVMs) for the facial recognition system.we u... [more] |
PRMU2013-126 CNR2013-34 pp.31-34 |