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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MBE, NC (Joint) |
2022-03-03 15:05 |
Online | Online | A Study on Personalization of the Head-Related Transfer Function in the Median Plane by Modifying the Notch Frequency Sayaka Kato, Susumu Kuroyanagi (NITech)  MBE2021-97 |
In order to enable highly accurate 3D sound image control, various researches have been conducted to obtain a Head-Relat... [more] | MBE2021-97 pp.43-46 |
| AP, SANE, SAT (Joint) |
2019-07-19 14:30 |
Miyagi | Tohoku Univ. | 2D Interpolation of GPR Common Midpoint Profile by Using Radial Basis Function Neural Networ k Changyu Zhou, Motoyuki Sato (Tohoku-dai) SANE2019-34 |
A new velocity analysis algorithm for Ground Penetrating Radar (GPR) is investigated. GPR provides the common mid-point ... [more] | SANE2019-34 pp.91-96 |
| NC, MBE (Joint) |
2012-12-12 10:40 |
Aichi | Toyohashi University of Technology | A numerical derivation of learning coefficient in radial basis function network Satoru Tokuda, Kenji Nagata, Masato Okada (Univ. of Tokyo) NC2012-78 |
Radial basis function (RBF) network is a regression model which regresses input-output data by radial basis functions su... [more] | NC2012-78 pp.25-30 |
| NC | 2010-10-23 11:55 |
Fukuoka | Kyushu Inst. Tech. (Kitakyushu Sci. and Res. Park) | A neural network model for multiple 3D object recognition Yasuaki Higuchi, Nobuhiko Asakura, Toshio Inui (Kyoto Univ.) NC2010-44 |
We propose a neural network model for recognition of multiple objects that extends the Generalized Radial Basis Function... [more] | NC2010-44 pp.11-16 |
| NLP | 2009-11-11 11:10 |
Kagoshima | Chaotic Time Series Prediction by Combining Echo-State Networks and Radial Basis Function Networks Yoshitaka Itoh, Masaharu Adachi (Tokyo Denki Univ.) NLP2009-86 |
In this report, we describe a chaotic time series prediction method by a network which combines echo state networks (ES... [more] |
NLP2009-86 pp.27-30 |
|
| NC, MBE (Joint) |
2009-03-12 13:25 |
Tokyo | Tamagawa Univ. | Covariate Shift and Incremental Learning Koichiro Yamauchi (Hokudai Univ.) NC2008-142 |
Learning strategies under `covariate shift' have recently been widely discussed. Under covariate shift, the density o... [more] |
NC2008-142 pp.231-236 |
| NLP | 2008-02-01 15:15 |
Hokkaido | Image Restoration and Interpolation by Radial Basis Function Network Zhenxing Pan, Nobumitsu Fujiwara, Shinji Doi, Sadatoshi Kumagai (Osaka Univ.) NLP2007-152 |
A radial basis function network (RBFN) can be applied to image restoration and interpolation. The RBFN is a linear combi... [more] | NLP2007-152 pp.57-62 |
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| NLP | 2007-06-08 14:15 |
Hiroshima | A very simple method for the image interpolation by radial basis function networks Zhenxing Pan, Masato Senoo, Shinji Doi, Sadatoshi Kumagai (Osaka Univ.) NLP2007-14 |
Given sampled data from an original image, an interpolated image can be generated by using a radial basis function netwo... [more] | NLP2007-14 pp.17-22 |
|
| MBE, NC (Joint) |
2005-12-09 14:40 |
Aichi | Performance comparison of autonomous mobile robot controllers focused on the difference of neural network architecture Makoto Motoki (Nagoya Institute of Technology), Seiichi Koakutsu, Hironori Hirata (Chiba Univ.) |
In this article, we report the experimental results of performance comparison of autonomous mobile robot controllers foc... [more] | NC2005-85 pp.25-30 |
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