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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| 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 |
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| 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 |
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| PRMU, CNR | 2014-02-13 15:00 |
Fukuoka | Improved Subspace-based Support Vector Machines by linear combination of the separating hyper-planes Shota Funaki, Takuya Kitamura (TNCT) PRMU2013-135 CNR2013-43 |
In this paper, we propose the improved subspace-based SVMs (SS-SVMs) by linearly-combining the separating hyper-planes (... [more] | PRMU2013-135 CNR2013-43 pp.77-82 |
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