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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MBE, NC (Joint) |
2022-03-02 09:30 |
Online | Online | A Study on Improvement of Recognition Accuracy and Speed-up of SOM-based Classification System Shun Tasaka, Hiroomi Hikawa (Kansai Univ.)  NC2021-46 |
This paper discusses a new type of image classifier called class-SOM, which is based on self-organizing map (SOM). The... [more] |
NC2021-46 pp.1-4 |
| MBE, NC (Joint) |
2022-03-02 15:45 |
Online | Online | NC2021-57 |
We propose a polarimetric remote sensing system to classify daily movements of humans such as walking and standing. We e... [more] | NC2021-57 pp.56-61 |
| IBISML | 2022-01-18 15:20 |
Online | Online | Determining the number of clusters using the shrinking maximum likelihood self-organizing map Ryosuke Motegi, Yoichi Seki (Gunma Univ.) IBISML2021-29 |
Determining the number of clusters is one of the major challenges in clustering. The conventional method, such as the Ex... [more] | IBISML2021-29 pp.81-87 |
| SIS | 2021-03-05 10:50 |
Online | Online | A trial of quantitative evaluation focused on area change in self-organizing map Yuto Nakashima, Hiroshi Wakuya (Saga Univ.), Fukuko Moriya (Kurume Univ.), Kaoru Araki, Hideaki Itoh (Saga Univ.) SIS2020-56 |
A self-organizing map (SOM) is one of the AI techniques to visualize an applied multi-dimensional data set onto the two-... [more] | SIS2020-56 pp.114-119 |
| NC, MBE (Joint) |
2021-03-04 16:25 |
Online | Online | Hierarchical Feature Extraction for Dynamic Q-Network Taishi Komatsu, Yukari Yamauchi (Nihon Univ.) NC2020-62 |
Recently, Convolutional Neural Networks (CNN), which have been successful in the field of image recognition, use a hiera... [more] | NC2020-62 pp.112-116 |
| MBE, NC, NLP, CAS (Joint) [detail] |
2020-10-30 15:45 |
Online | Online | A Proposal of Self-Organizing Map Based on Attribute Information with Attenuate Rate Tetsuya Sato, Yukari Yamauti (Nihon Univ.) NC2020-23 |
Self-organizing Maps(SOM) is a simple algorithm, has excellent clustering capabilities, and can create a nonlinear model... [more] | NC2020-23 pp.77-82 |
| NLP | 2020-05-15 11:25 |
Online | Online | Facial Expression Recognition by a Neural Network Inspired from Processing between the Visual Cortex and Amygdala Daiki Yoshihara, Toshikazu Samura (Yamaguchi Univ.) NLP2020-2 |
Facial expressions are important to communication. The visual cortex and amygdala are involved in the recognition of fac... [more] | NLP2020-2 pp.7-10 |
| NC, MBE (Joint) |
2020-03-05 13:50 |
Tokyo | University of Electro Communications (Cancelled but technical report was issued) |
A Proposal of Self-Organizing Maps Based on Learning with Attribute Information Tetsuya Sato, Yukari Yamauti (Nihon Univ.) NC2019-96 |
Self-organizing maps(SOM) is a simple algorithm, and has excellent clustering capabilities. However, since SOM performs ... [more] | NC2019-96 pp.119-124 |
| EMT, IEE-EMT | 2019-11-07 15:15 |
Saga | Hotel Syunkeiya | Land classification using unsupervised quaternion neural network with neighbor pixel information Jungmin Song, Ryo Natusaki, Akira Hirose (The Univ. of Tokyo) EMT2019-57 |
(To be available after the conference date) [more] | EMT2019-57 pp.117-122 |
| MBE, NC | 2019-10-12 10:50 |
Miyagi | An Optimization for Classification by Self-Organizing Maps Based on Attribute Information Tetsuya Sato (Nihon Univ.), Kazuma Tsuchida (STUDIO ONE OR EIGHT), Yukari Yamauti (Nihon Univ.) MBE2019-41 NC2019-32 |
Self-Organizing Map (SOM) is a simple algorithm that has excellent clustering capabilities and adapts continuous changes... [more] | MBE2019-41 NC2019-32 pp.59-63 |
|
| IA, ICSS | 2019-06-07 11:20 |
Miyagi | Research Institute for Electrical Communication, Tohoku University | Development of System to Analyze Aggressive Communication Using Self-Organizing Map and Convolutional Neural Network Akifumi Iwasa, Hikofumi Suzuki, Takumi Uchiyama (Shinshu Univ.), Tetsuya ui (NEC) IA2019-8 ICSS2019-8 |
In recent years, the importance of the Internet is increasing. However, DoS / DDoS attacks is increasing. It is difficul... [more] | IA2019-8 ICSS2019-8 pp.37-41 |
| CQ, ICM, NS, NV (Joint) |
2018-11-16 09:15 |
Ishikawa | Development of System to Analyze Advanced Attacks Using Self-Organizing Map Akifumi Iwasa, Hikohmi Suzuki (Shinshu Univ.), Tetsuya Ui (NEC) NS2018-140 |
In recent years, the importance of the Internet is increasing. However, DoS / DDoS attacks is increasing. It is difficul... [more] | NS2018-140 pp.57-61 |
|
| MBE, NC (Joint) |
2018-03-14 10:00 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Hierarchical quaternion neural networks with self-organizing codebook for unsupervised PolSAR land classification Hyunsoo Kim, Akira Hirose (Tokyo Univ.) NC2017-88 |
We propose a self-organizing codebook-based hierarchical polarization feature vector generation to realize an unsupervis... [more] | NC2017-88 pp.121-126 |
| MBE, NC, NLP (Joint) |
2018-01-26 15:50 |
Fukuoka | Kyushu Institute of Technology | Restricted Representation for Attentional Items of Feature Map Developed by a Self-Organizing Map Hiroshi Wakuya, Yuukou Tanaka, Hideaki Itoh (Saga Univ.) NC2017-56 |
A self-organizing map (SOM) can be seen as a signal converter preserving its topology between the input and output space... [more] | NC2017-56 pp.35-40 |
| EMT, IEE-EMT | 2017-11-09 10:50 |
Yamagata | Tendo Hotel (Tendo, Yamagata) | Flexible Unsupervised PolSAR Land Classification System Based on Quaternion Neural Networks Hyunsoo Kim, Akira Hirose (Tokyo Univ.) EMT2017-48 |
We propose a flexible unsupervised PolSAR land classification system based on quaternion neural networks. The existing ... [more] | EMT2017-48 pp.37-42 |
| MBE, NC (Joint) |
2017-10-07 15:40 |
Osaka | Osaka Electro-Communication University | Learning Characteristics of Self-Organizing Map with Adaptive Neighborhood Function Hikari Yoshimi, Hidetaka Ito, Hiroomi Hikawa (Kansai Univ.) NC2017-23 |
This paper proposes a new neighborhood function for the self-organizing map(SOM).As the learning of the SOM progresses,... [more] | NC2017-23 pp.19-24 |
| SANE | 2017-10-05 14:20 |
Tokyo | Maison franco - japonaise (Tokyo) | Unsupervised Adaptive PolSAR Land Classification System Using Quaternion Neural Networks Hyunsoo Kim, Akira Hirose (Univ. of Tokyo) SANE2017-57 |
We propose an unsupervised adaptive PolSAR land classification system using quaternion neural networks. Most of the exis... [more] | SANE2017-57 pp.73-78 |
| NLP | 2016-12-12 15:25 |
Aichi | Chukyo Univ. | Visualization and Classification by ElasticSOM Yuto Take, Pitoyo Hartono (Chukyo Univ.) NLP2016-89 |
Due to its simplicity, Self-Organizing Maps(SOM) are often utilized to visualize high dimensional data. While SOM is abl... [more] | NLP2016-89 pp.27-32 |
| NC | 2015-01-29 16:05 |
Fukuoka | Kyushu Institute of Technology | Robustness of Tensor SOM for Missing Data Yasuhiro Wakita, Toru Iwasaki, Tetsuo Furukawa (Kyutech) NC2014-61 |
Tensor SOM is an extension of the self-organizing map (SOM), which enables us to visualize simultaneous visualization of... [more] | NC2014-61 pp.21-26 |
| EMM, EA | 2014-11-20 16:30 |
Fukuoka | Bit-error-tolerant quantizer based on self organizing map Akinori Ito (Tohoku Univ.) EA2014-31 EMM2014-59 |
Bit errors cannot be avoided when communicating using a digital channel. Packet-based communication abodons the packets ... [more] | EA2014-31 EMM2014-59 pp.19-24 |
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