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Committee Date Time Place Paper Title / Authors Abstract Paper #
MSS, SS 2023-01-11
(Primary: On-site, Secondary: Online)
Improvement of Composite-SVM in Hyperspectral Image 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
TL 2018-10-28
Hokkaido National Institute of Technology, Hakodate College An investigation of identifier naming strongly linked to specific pattern of program structure
Yoshiki Mashima (O.E.C.U.), Sachio Hirokawa (Kyushu Univ.), Kazuhiro Takeuchi (O.E.C.U.) TL2018-40
Identifiers in programming language such as variable names, class names, and method names are generally given in natural... [more] TL2018-40
CPSY, DC, IPSJ-ARC [detail] 2018-06-15
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
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] 2018-06-13
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
PRMU, BioX 2017-03-21
Aichi   [Short Paper] Bile duct segmentation from 3D CT image based on machine learning and probability map-assisted region growing
Pengfei Chen, Hiroshi Tanaka, Masahiro Oda, Holger Roth, Tsuyoshi Igami, Masato Nagino, Kensaku Mori (NU) BioX2016-55 PRMU2016-218
In this paper, we present our study on the bile duct segmentation from 3D CT volumes. In hepatobiliary surgery, it is re... [more] BioX2016-55 PRMU2016-218
PRMU, CNR 2017-02-19
Hokkaido   3D Generic Object Recognition based on Score Level Fusion via Superquadric Representation
Ryo Hachiuma, Yuko Ozasa, Hideo Saito (Keio Univ.) PRMU2016-175 CNR2016-42
Our goal is to recognize 3d generic objects and estimate object's shape for object grasping simultaneously.
In this pap... [more]
PRMU2016-175 CNR2016-42
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
PRMU 2015-12-22
Nagano   Development and comparing of personal authentication systems by signature in the air
Daiki Yamada, Takuya Kitamura (NIT) PRMU2015-110
In late years biological distinction has been used for security systems because it has high reliabiliby. Biometric featu... [more] PRMU2015-110
IBISML 2015-11-26
Ibaraki Epochal Tsukuba [Poster Presentation] Robustification of Learning Algorithms using Hinge-loss
Takafumi Kanamori (Nagoya Univ.), Shuhei Fujiwara (TopGate), Akiko Takeda (Univ. of Tokyo) IBISML2015-71
We propose a unified formation of robust learning methods for classification and regression problems.
In the learnin... [more]
PRMU, IBISML, IPSJ-CVIM [detail] 2014-09-01
Ibaraki   Neutralized Empirical Risk Minimization with Covariance-based Neutrality Risk
Kazuto Fukuchi, Jun Sakuma (Univ. of Tsukuba) PRMU2014-48 IBISML2014-29
In order to apply machine learning algorithms to real world problems, it is necessary to ensure that discrimination, unf... [more] PRMU2014-48 IBISML2014-29
PRMU, CNR 2014-02-13
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
PRMU, CNR 2014-02-13
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
IBISML 2013-11-12
Tokyo Tokyo Institute of Technology, Kuramae-Kaikan [Poster Presentation] Support vector comparison machines
Toby Dylan Hocking, Supaporn Spanurattana, Masashi Sugiyama (Tokyo Inst. of Tech.) IBISML2013-51
In ranking problems, the goal is to learn a ranking function
$r(x)inRR$ from labeled pairs $x,x'$ of input points. In... [more]
AI, SC 2013-08-09
Tokyo National Institute of Informatics Feature Extraction and Selection for Detecting Manipulation Online Review
Ching-Yun Hsueh (Univ. of Aizu), Long-Sheng Chen (Chaoyang Univ. of Tech.), Qiangfu Zhao (Univ. of Aizu) AI2013-16 SC2013-10
with the proliferation of e-commerce, internet has become an excellent platform for gathering and sharing consumers’ per... [more] AI2013-16 SC2013-10
Tokyo Tamagawa University Application of Support Vector Machines and Particle Swarm Optimization in the classification of auditory event-related potentials
Alejandro Gonzalez, Isao Nambu, Haruhide Hokari, Yasuhiro Wada (Nagaoka Univ. of Tech.) NC2012-173
The accurate detection of single-trial event-related potentials (ERP) is very important for the development of practical... [more] NC2012-173
IBISML 2012-03-13
Tokyo The Institute of Statistical Mathematics A Study on Decision Boundary Stability in Active Learning with Support Vector Machine
Takeru Takahashi, Yuji Waizumi, Kazuo Hashimoto (Tohoku Univ.) IBISML2011-101
Studies on active learning have advanced in order to improve the performance of supervised learning machines
efficientl... [more]
WIT 2012-03-09
Ibaraki   Human Interface Using EMG-Based Motion Recognition Method
Masahiro Yoshikawa, Yoshio Matsumoto (AIST) WIT2011-80
In this paper, we report an EMG-based motion recognition method using support vector machines (SVMs). This method uses E... [more] WIT2011-80
PRMU, SP 2012-02-10
Miyagi   Event detection from Video using GMM-Supervectors and SVMs
Yusuke Kamishima, Nakamasa Inoue, Koichi Shinoda (Tokyo Tech), Shunsuke Sato (Canon) PRMU2011-230 SP2011-145
In multimedia event detection, complex target events are detected from a large set of consumer domain videos taken in un... [more] PRMU2011-230 SP2011-145
MVE, HI-SIG-VR 2011-10-14
Hokkaido   Unicursal Gesture Recognition with Support Vector Machines Using Distance to Borders
Bessie Chan, Ryosuke Aoki, Masayuki Ihara, Minoru Kobayashi, Toru Kobayashi (NTT), Shingo Kagami (Tohoku Univ.) MVE2011-54
Vision-based recognition systems using hand gestures to control applications like TV menus are gaining popularity in the... [more] MVE2011-54
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-05
Hokkaido   Learning of Kernel Classfier based on General Loss Minimization
Masato Ishii, Atsushi Sato (NEC) PRMU2011-61 IBISML2011-20
This paper presents a new method for learning kernel classifiers. First, we formulate a novel learning scheme called ``G... [more] PRMU2011-61 IBISML2011-20
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