Tuning support vector machines for improving four-class emotion classification in virtual reality (VR) using heart rate features
The main objective of this paper is to conduct three experiments using Support Vector Machine (SVM) with different parameter settings to find and compare the accuracy of each SVM setting. Heart rate (HR) signals were collected with a medical-grade wearable heart rate monitor from Empatica (E4 Wristb...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English English |
| Published: |
IOP Publishing
2020
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| Subjects: | |
| Online Access: | https://eprints.ums.edu.my/id/eprint/27010/1/Tuning%20support%20vector%20machines%20for%20improving%20four-class%20emotion%20classification%20in%20virtual%20reality%20%28vr%29%20using%20heart%20rate%20features%20.pdf https://eprints.ums.edu.my/id/eprint/27010/2/Tuning%20support%20vector%20machines%20for%20improving%20four-class%20emotion%20classification%20in%20virtual%20reality%20%28vr%29%20using%20heart%20rate%20features%201.pdf |
Internet
https://eprints.ums.edu.my/id/eprint/27010/1/Tuning%20support%20vector%20machines%20for%20improving%20four-class%20emotion%20classification%20in%20virtual%20reality%20%28vr%29%20using%20heart%20rate%20features%20.pdfhttps://eprints.ums.edu.my/id/eprint/27010/2/Tuning%20support%20vector%20machines%20for%20improving%20four-class%20emotion%20classification%20in%20virtual%20reality%20%28vr%29%20using%20heart%20rate%20features%201.pdf