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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SP, IPSJ-MUS, IPSJ-SLP [detail] | 2022-06-17 13:00 |
Online | Online | Study and Comparison of Direction Estimation Methods for Instrumental Sound Sources Kaho Yamamoto, Akio Ogihara (Kindai Univ.), Harumi Murata (Chukyo Univ.)  SP2022-3 |
Surround sound is becoming more and more familiar as digital contents such as TV broadcasting support surround sound. Ho... [more] | SP2022-3 pp.7-9 |
| CS, CQ (Joint) |
2021-05-14 09:50 |
Online | On-line | Proposal of Channel Matrix Estimation by Using Neural Networks on Wavelength Division Multiplexing Optical Wireless Communication Masaki Inomata, Saki Shibata, Takanori Iwamatsu, Saeko Oshiba (KIT) CS2021-8 |
This paper describes a wavelength division multiplexing (WDM) - optical wireless communication (OWC) system using orth... [more] |
CS2021-8 pp.25-30 |
| NC, MBE (Joint) |
2014-03-18 10:20 |
Tokyo | Tamagawa University | Theory of firing distribution with common noise originating from synaptic connectivity Ryo Karakida, Yasuhiko Igarashi, Kenji Nagata (Univ. of Tokyo), Masato Okada (Univ. Tokyo/RIKEN) NC2013-103 |
Common noise is a correlated noise input shared by multiple neurons. Biological experiments have shown that the common n... [more] | NC2013-103 pp.85-90 |
| NC, NLP | 2011-01-24 09:30 |
Hokkaido | Hokakido Univ. | Parameterized online quasi-Newton Training Algorithm for Feedforward Neural Networks Hiroshi Ninomiya (SIT) NLP2010-125 NC2010-89 |
This paper describes a new gradient based technique for training of feedforward neural networks. Recently, improved onli... [more] | NLP2010-125 NC2010-89 pp.1-6 |
| NC, MBE (Joint) |
2010-03-09 17:40 |
Tokyo | Tamagawa University | A Framework of Parallel and Flexible Learning Control System Using Reinforcement Learning and a Recurrent Neural Network Satoshi Takatsu, Kenta Goto, Katsunari Shibata (Oita Univ.) NC2009-114 |
The authors think that constructing the whole process from sensors to motors by a neural network and learning it by rein... [more] | NC2009-114 pp.155-160 |
| MSS | 2008-08-04 15:30 |
Shizuoka | Shizuoka University (Hamamatsu Campus), Faculty of Engineering | A Hybrid PSO and quasi-Newton Technique for Training of Feedforward Neural Networks Hiroshi Ninomiya (Shonan Inst. of Tech.), Qi-Jun Zhang (Carleton Univ.) CST2008-17 |
This paper describes a new technique for training feedforward neural networks. We employ the proposed algorithm for robu... [more] | CST2008-17 pp.29-34 |
| NC | 2007-03-14 10:40 |
Tokyo | Tamagawa University | Optimization of the support vector machine's kernels using a feedforward neural network Shin'ichi Tamura (DENSO CORP.) |
A novel approach to determining the optimal kernel parameters of Support Vector Machines is proposed. Based on the fact ... [more] | NC2006-137 pp.115-120 |
| NC | 2007-03-14 13:00 |
Tokyo | Tamagawa University | Effect of Sparse Encoding in the Layered Associative Network Kazuya Ishibashi (Univ. Tokyo), Kosuke Hamaguchi (RIKEN), Masato Okada (Univ. Tokyo/ RIKEN) |
A synfire chain is a simple neural network model which can generate stable synchronous firings called a pulse packet and... [more] | NC2006-141 pp.139-144 |
| NC | 2006-03-16 10:20 |
Tokyo | Tamagawa University | On the Realization of a Large Margin Non-linear Classifier Using Feed-forward Neural Networks Shin'ichi Tamura, Makoto Sakai (DENSO) |
It is well known that a feedforward neural network with infinitely many hidden yunits has the capability of realizing an... [more] | NC2005-139 pp.85-89 |
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