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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| PRMU | 2022-12-15 10:45 |
Toyama | Toyama International Conference Center (Primary: On-site, Secondary: Online) |
DN4C
-- An Interactive Image Segmentation System Combining Deep Neural Network and Nearest Neighbor Classifier -- Toshikazu Wada, Koji Kamma (Wakayama University)  PRMU2022-35 |
Color/texture based image segmentation can be widely applied to the images for product and/or medical inspection, remote... [more] | PRMU2022-35 pp.19-24 |
| ITS, IE, ITE-MMS, ITE-HI, ITE-ME, ITE-AIT [detail] | 2019-02-20 11:00 |
Hokkaido | Hokkaido Univ. | A Note on Estimation of Rock Drilling Energy Using Tunnel Working Face Images Kentaro Yamamoto, Ryosuke Harakawa, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
In tunnel construction, it is important to grasp the geological condition of the rock to shorten the construction period... [more] | |
| SANE | 2015-11-24 13:45 |
Overseas | AIT, Bangkok, Thailand | Adaptive Nearest Feature Space Method for Remote Sensing Images Classification Yang-Lang Chang (NTUT), Chihyuan Chu (G-AVE), Hirokazu Kobayashi (OIT), Tzu-Wei Tseng (NTUT) SANE2015-62 |
In this paper a novel technique based on nearest feature space (NFS), known as adaptive nearest feature space (ANFS), is... [more] | SANE2015-62 pp.71-74 |
| PRMU | 2013-02-22 10:00 |
Osaka | Grafting Trees A Nearest Neighbor Srarch Algorithm without Distance Computation Yohei Ohtani, Toshikazu Wada, Hiroshi Oike (Wakayama Univ.) PRMU2012-162 |
This report presents a nearest neighbor search algorithm without distance computation. K-D tree based nearest neighbor s... [more] | PRMU2012-162 pp.137-142 |
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| IBISML, PRMU, IPSJ-CVIM [detail] | 2010-09-05 17:30 |
Fukuoka | Fukuoka Univ. | A Study on Feature Selection Path for High-Dimensional Local Classifiers Ichiro Takeuchi (NIT) PRMU2010-71 IBISML2010-43 |
We study feature selection and weighting problems for local-based classifier. The proposed algorithm is formulated as a ... [more] | PRMU2010-71 IBISML2010-43 pp.105-112 |
| PRMU, IE, MI | 2010-05-14 14:45 |
Aichi | Chubu Univ. | An Analysis of the Impact of a Training Dataset Expansion for Generic Object Recognition Takumi Toyama, Koichi Kise (Osaka Prefecture Univ.) IE2010-40 PRMU2010-28 MI2010-28 |
In the field of pattern recognition, it is well known that the contents of a training dataset affects the recognition pe... [more] | IE2010-40 PRMU2010-28 MI2010-28 pp.145-150 |
| PRMU | 2007-12-13 15:00 |
Hyogo | Kobe Univ. | Pose Recognition with the NNC-Tree Jie Ji, Naoki Tominaga, Akiha Iwase, Yoshihiko Watanabe, Kazuhiko Hirakuri, Qiangfu Zhao (Univ. of Aizu) PRMU2007-142 |
Abstract: Pose recognition is important in many practical applications. For example, a driver assistance system can dete... [more] | PRMU2007-142 pp.37-42 |
| NC | 2007-06-14 11:45 |
Okinawa | OIST Seaside House | A Study on Significance Analysis of Gene Sets Using Nearest-Neighbor Classification Error Ichiro Takeuchi, Shintaro Hayashi (Mie Univ.), Miyuki Suguro, Masao Seto (Aichi Cancer Center) NC2007-13 |
Relating gene expression profiles from microarray experiments with biological knowledge databases is an important step f... [more] | NC2007-13 pp.29-34 |
| PRMU, NLC | 2005-09-21 09:00 |
Tokyo | Fast Semi-Supervised Learning on Nearest Neighbor Graphs Weiwei Du, Kiichi Urahama (Kyushu Univ.) |
A simple label propagation technique is presented for semi-supervised learning of pattarn classifiers. In our technique,... [more] | NLC2005-24 PRMU2005-51 pp.1-6 |
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| IE, ITE-BCT, ITE-AIT, ITE-ME | 2004-11-26 09:30 |
Nagasaki | Pattern Classification Using Distance Weighted Average Pattern of k Nearest Neighbors Seiji Hotta, Senya Kiyasu, Sueharu Miyahara (Nagasaki Univ.) |
The recognition rate of the typical nonparametric method ``$k$-Nearest Neighbor rule ($k$NN)" is degraded when the dimen... [more] | IE2004-86 pp.1-4 |
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| PRMU | 2004-11-19 14:15 |
Fukui | An new method for efficient design of neural network trees Qiangfu Zhao (U-Aizu) |
Neural network tree (NNTree) is a hybrid learning model with the overall structure being a decision tree (DT), and each... [more] |
PRMU2004-115 pp.59-64 |
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