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Neural Networks for Emotion Classification
[article]
2011
arXiv
pre-print
It is argued that for the computer to be able to interact with humans, it needs to have the communication skills of humans. One of these skills is the ability to understand the emotional state of the person. This thesis describes a neural network-based approach for emotion classification. We learn a classifier that can recognize six basic emotions with an average accuracy of 77% over the Cohn-Kanade database. The novelty of this work is that instead of empirically selecting the parameters of
arXiv:1105.6014v1
fatcat:ck3x6tl3vfgxvlg5ad5rawrmuq