| _version_ |
1860799923335725056
|
| building |
INTELEK Repository
|
| collection |
Online Access
|
| collectionurl |
https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
|
| date |
2018-05-14 04:25:40
|
| format |
Restricted Document
|
| id |
7935
|
| institution |
UniSZA
|
| originalfilename |
3726-01-FH05-FSSG-18-13764.pdf
|
| person |
Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML
like Gecko) Chrome/65.0.3325.181 Safari/537.36
|
| recordtype |
oai_dc
|
| resourceurl |
https://intelek.unisza.edu.my/intelek/pages/view.php?ref=7935
|
| spelling |
7935 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=7935 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Book Chapter application/pdf 2 1.6 Adobe Acrobat Pro DC 20 Paper Capture Plug-in Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML like Gecko) Chrome/65.0.3325.181 Safari/537.36 2018-05-14 04:25:40 3726-01-FH05-FSSG-18-13764.pdf UniSZA Private Access The application of support vector machine in classifying potential archers using bio-mechanical indicators This study classifies potential archers from a set of bio-mechanical indicators trained via different Support Vector Machine (SVM) models. 50 youth archers drawn from a number of archery programmes completed a one end archery shooting score test. Bio-mechanical evaluation of postural sway, bow movement, muscles activation of flexor and extensor as well as static balance were recorded. k-means clustering technique was used to cluster the archers based on the indicators tested. Fine, medium and coarse radial basis function kernel-based SVM models were trained based on the measured indicators. The five-fold cross-validation technique was utilised in the present investigation. It was shown from the present study, that the employment of SVM is able to assist coaches in identifying potential athletes in the sport of archery. © 2018, Springer Nature Singapore Pte Ltd. Pleiades Publishing Pleiades Publishing 385-391 Lecture Notes in Mechanical Engineering
|
| spellingShingle |
The application of support vector machine in classifying potential archers using bio-mechanical indicators
|
| summary |
This study classifies potential archers from a set of bio-mechanical indicators trained via different Support Vector Machine (SVM) models. 50 youth archers drawn from a number of archery programmes completed a one end archery shooting score test. Bio-mechanical evaluation of postural sway, bow movement, muscles activation of flexor and extensor as well as static balance were recorded. k-means clustering technique was used to cluster the archers based on the indicators tested. Fine, medium and coarse radial basis function kernel-based SVM models were trained based on the measured indicators. The five-fold cross-validation technique was utilised in the present investigation. It was shown from the present study, that the employment of SVM is able to assist coaches in identifying potential athletes in the sport of archery. © 2018, Springer Nature Singapore Pte Ltd.
|
| title |
The application of support vector machine in classifying potential archers using bio-mechanical indicators
|
| title_full |
The application of support vector machine in classifying potential archers using bio-mechanical indicators
|
| title_fullStr |
The application of support vector machine in classifying potential archers using bio-mechanical indicators
|
| title_full_unstemmed |
The application of support vector machine in classifying potential archers using bio-mechanical indicators
|
| title_short |
The application of support vector machine in classifying potential archers using bio-mechanical indicators
|
| title_sort |
application of support vector machine in classifying potential archers using bio-mechanical indicators
|