Affective computing
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving SVM accuracy by training on auxiliary data sources
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ECML '07 Proceedings of the 18th European conference on Machine Learning
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Preference learning for cognitive modeling: a case study on entertainment preferences
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Affective Computing
Active class selection for arousal classification
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Active class selection for arousal classification
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
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Although psychophysiological and affective computing approaches may increase facility for development of the next generation of human-computer systems, the data resulting from research studies in affective computing include large individual differences. As a result, it is important that the data gleaned from an affective computing system be tailored for each individual user by re-tuning it using user-specific training examples. Given the often time-consuming and/or expensive nature of efforts to obtain such training examples, there is a need to either 1) minimize the number of user-specific training examples required; or 2) to maximize the learning performance through the incorporation of auxiliary training examples from other subjects. In [11] we have demonstrated an active class selection approach for the first purpose. Herein we use transfer learning to improve the learning performance by combining user-specific training examples with auxiliary training examples from other subjects, which are similar but not exactly the same as the user-specific training examples. We report results from an arousal classification application to demonstrate the effectiveness of transfer learning in a Virtual Reality Stroop Task designed to elicit varying levels of arousal.