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Committee Date Time Place Paper Title / Authors Abstract Paper #
PRMU, CNR 2017-02-18
Hokkaido   Small-sized Kernel Classifier By Support Vector Retraining Based on Minimum Classification Error Criterion
Ryoma Tani (Doshisha Univ.), Hideyuki Watanabe (ATR), Shigeru Katagiri, Miho Osaki (Doshisha Univ.) PRMU2016-159 CNR2016-26
Different from the Multi-class Support Vector Machine (MSVM) that fixes Support Vectors (SVs) to training samples, the K... [more] PRMU2016-159 CNR2016-26
SIS 2016-03-11
Tokyo Tokyo City Univ. A Feasibility Study of Scene Text Recognition Based on Filtered Channel Features
Rui Kasuya, Takuro Oki, Ryusuke Miyamoto (Meiji Univ.) SIS2015-65
Scene Text Recognition is one of the most challenging task in the field of the image recognition, recently.
Several kin... [more]
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
MI 2012-01-20
Okinawa   A study on automated anatomical labeling to abdominal arteries in 3D abdominal CT images by using multil-class AdaBoost -- Improvement of classifiers of the artery names --
Bui Huy Hoang, Masahiro Oda, Yukitaka Nimura (Meidai), Takayuki Kitasaka (Aichi Institute of Technology), Kazunari Misawa (Aichi Cancer Center Hospital), Michitaka Fujiwara, Kensaku Mori (Meidai) MI2011-148
We have developed an automated anatomical labeling method for the abdominal arteries to support understanding of the str... [more] MI2011-148
SIS 2011-06-10
Okinawa Ishigaki Health & Wellness Center Surface Layout Recognition using Combination of Visual Cues based on Multiple Segmentations
Jaehoon Yu (Osaka Univ.), Ryusuke Miyamoto (NAIST), Takao Onoye (Osaka Univ.) SIS2011-20
To realize the technology of driving assistance and autonomous locomotion
based on image processing, it is indispensabl... [more]
Tokyo Tamagawa Univ A hierarchical multi-class classification method based on error-correcting output coding
Yoshimi Oyama (Kyoto Univ.), Takashi Takenouchi (NAIST), Shin Ishii (Kyoto Univ.) NC2007-169
There have been studies of multi-class classification by combining multiple binary classifiers within the framework of e... [more] NC2007-169
 Results 1 - 6 of 6  /   
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