Abstract
Emotion recognition is one of the key steps towards emotional intelligence in advanced human-machine interaction. This paper adopted principal component analysis (PCA) to dimensionality reduction and combined AdaBoost algorithm to be served as classifier. Experimental result shows that the classifier performance was effective and steady. Emotion recognition impression was fairish and reasonable for special affective state groupings.
This work is supported by Chongqing Three Gorges University Foundation (2008-sxxyqn-29), China.
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Cheng, B. (2011). Emotion Recognition from Physiological Signals Using AdaBoost. In: Zeng, D. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23214-5_54
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DOI: https://doi.org/10.1007/978-3-642-23214-5_54
Publisher Name: Springer, Berlin, Heidelberg
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