Recognition of emotions using multimodal physiological signals and an ensemble deep learning model

@article{Yin2017RecognitionOE,
  title={Recognition of emotions using multimodal physiological signals and an ensemble deep learning model},
  author={Zhong Yin and Mengyuan Zhao and Yongxiong Wang and Jingdong Yang and Jianhua Zhang},
  journal={Computer methods and programs in biomedicine},
  year={2017},
  volume={140},
  pages={93-110}
}
BACKGROUND AND OBJECTIVE Using deep-learning methodologies to analyze multimodal physiological signals becomes increasingly attractive for recognizing human emotions. However, the conventional deep emotion classifiers may suffer from the drawback of the lack of the expertise for determining model structure and the oversimplification of combining multimodal feature abstractions. METHODS In this study, a multiple-fusion-layer based ensemble classifier of stacked autoencoder (MESAE) is proposed… CONTINUE READING
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