Paper Abstract and Keywords |
Presentation |
2020-10-22 09:30
Shared dataset framework toward applications of machine learning and mathematical optimization for 6G Kazuki Maruta (Tokyo Tech.), Yuta Ida (Yamaguchi Univ.), Yafei Hou (Okayama Univ.), Osamu Muta (Kyushu Univ.), Hiraku Okada (Nagoya Univ.), Toshihiko Nishimura (Hokkaido Univ.), Eiji Okamoto (Nagoya Inst. of Tech.), Yukitoshi Sanada (Keio Univ.), Hidekazu Murata (Kyoto Univ.), Satoshi Denno (Okayama Univ.) RCS2020-92 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In the research of applying machine learning and mathematical optimization to problems in wireless communication systems, datasets for training are very important, and an effort to store and release datasets for long term storage has recently started in overseas conferences. On the other hand, if a dataset suitable for research purposes is not available, efforts to create a dataset with wide cooperation are required. This paper presents a conceptual framework for collaborative efforts to create datasets and research using those datasets. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Data set / Machine learning / Mathematical optimization / 6G / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 120, no. 204, RCS2020-92, pp. 1-6, Oct. 2020. |
Paper # |
RCS2020-92 |
Date of Issue |
2020-10-15 (RCS) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
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RCS2020-92 |
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