Tuning support vector machines for improving four-class emotion classification in virtual reality (VR) using heart rate features

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...

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מידע ביבליוגרפי
Main Authors: Aaron Frederick Bulagang, James Mountstephens, Teo, Jason Tze
פורמט: Article
שפה:English
English
יצא לאור: IOP Publishing 2020
נושאים:
QA Mathematics
T Technology (General)
גישה מקוונת: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

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