Analysis of Team Relationship using Self-Organizing Map for University Volleyball Players
- DOI
- 10.2991/jrnal.2018.5.3.12How to use a DOI?
- Keywords
- Sports science; SOM; machine learning; clustering
- Abstract
In Japan, sports efforts are actively being carried out to host the 2020 Olympic Games. Especially in the field of sports science, researches on ergonomics, development of sports equipment and pattern recognition technology using artificial intelligence are actively researched. In previous research, we developed a clustering algorithm for positioning adaptation and relationships in team sports using self-organizing maps in university rugby players. However, I have not yet confirmed whether the developed algorithm can be applied to other team sports. For this reason, we applied the same algorithm to a university volleyball player. Then, as an algorithm, we verify whether it can be generally used for team sports.
- Copyright
- © 2018 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).
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TY - JOUR AU - Yasunori Takemura AU - Kazuya Oda AU - Michiyoshi Ono PY - 2018 DA - 2018/12/01 TI - Analysis of Team Relationship using Self-Organizing Map for University Volleyball Players JO - Journal of Robotics, Networking and Artificial Life SP - 199 EP - 203 VL - 5 IS - 3 SN - 2352-6386 UR - https://doi.org/10.2991/jrnal.2018.5.3.12 DO - 10.2991/jrnal.2018.5.3.12 ID - Takemura2018 ER -