Inductive Transfer Learning for Handling Individual Differences in Affective Computing (bibtex)
	title = {Inductive {Transfer} {Learning} for {Handling} {Individual} {Differences} in {Affective} {Computing}},
	volume = {6975},
	url = {},
	abstract = {Although psychophysiological and affective computing ap- proaches 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 train- ing 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 demon- strated an active class selection approach for the first purpose. Herein we use transfer learning to improve the learning performance by com- bining 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 classifi- cation application to demonstrate the effectiveness of transfer learning in a Virtual Reality Stroop Task designed to elicit varying levels of arousal.},
	booktitle = {Lecture {Notes} in {Computer} {Science}},
	author = {Wu, Dongrui and Parsons, Thomas D.},
	year = {2011},
	keywords = {MedVR},
	pages = {142--151}
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