The classification of skateboarding tricks by means of support vector machine: An evaluation of significant time-domain features

The classification of skateboarding tricks by means of support vector machine: An evaluation of significant time-domain features

This study aims to improve classification accuracy of different Support Vector Machine (SVM) models in classifying flat ground tricks namely Ollie, Kick-flip, Shove-it, Nollie and Frontside 180 through the identification of significant time-domain features. An amateur skateboarder (23 years of age ±...

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Detalhes bibliográficos
Principais autores: Muhammad Amirul, Abdullah, Muhammad Ar Rahim, Ibrahim, Muhammad Nur Aiman, Shapiee, Anwar P. P, Abdul Majeed, Mohd Azraai, Mohd Razman, Rabiu Muazu, Musa, Muhammad Aizzat, Zakaria
Formato: Capítulo de Livro
Idioma:English
Publicado em: Springer 2020
Assuntos:
T Technology (General)
Acesso em linha:http://umpir.ump.edu.my/id/eprint/32613/1/CONFERENCE%20%282%29%20-%20The%20classification%20of%20skateboarding%20tricks%20by%20means%20of%20support%20vector%20machine%20an%20evaluation%20of%20significant%20time-domain%20features.pdf

Internet

http://umpir.ump.edu.my/id/eprint/32613/1/CONFERENCE%20%282%29%20-%20The%20classification%20of%20skateboarding%20tricks%20by%20means%20of%20support%20vector%20machine%20an%20evaluation%20of%20significant%20time-domain%20features.pdf

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