By Arvid Kappas (auth.), Sidney D’Mello, Arthur Graesser, Björn Schuller, Jean-Claude Martin (eds.)

The two-volume set LNCS 6974 and LNCS 6975 constitutes the refereed court cases of the Fourth foreign convention on Affective Computing and clever interplay, ACII 2011, held in Memphis,TN, united states, in October 2011.
The one hundred thirty five papers during this quantity set awarded including three invited talks have been rigorously reviewed and chosen from 196 submissions. The papers are prepared in topical sections on popularity and synthesis of human impact, affect-sensitive purposes, methodological concerns in affective computing, affective and social robotics, affective and behavioral interfaces, appropriate insights from psychology, affective databases, overview and annotation tools.

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Extra info for Affective Computing and Intelligent Interaction: 4th International Conference, ACII 2011, Memphis, TN, USA, October 9–12, 2011, Proceedings, Part I

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625). 583). Table 2. 2 Day Cross Validation Classification Results (Static Classifiers) The basic assumption behind using classification techniques to model affective physiological data is that pre-trained models can be used to predict affect from future unseen input. A day cross validation procedure was then devised, where training data comprised data from four days and the fifth day data was used for testing. This procedure was repeated five times to test on all day datasets. The objective of this analysis is to assess the accuracy of classifiers that are trained on different day data to predict exemplars from other days.

6 O. Alzoubi et al. There are different strategies used for building and updating classifier ensembles that can work in non-stationary environments; see [12] for a detailed review. Winnow is an ensemble based algorithm that is similar to a weighted majority voting algorithm because it combines decisions from ensemble members based on their weights. However, it utilizes a different updating approach for member classifiers. This includes promoting ensemble members that make correct predictions and demoting those that make incorrect predictions.

91). 46). Clearly, the IAPS stimuli was quite successful in eliciting valence, but was much less effective in influencing arousal. 1 Classification Results for Day Datasets Day datasets were constructed separately for the two affective measures valence (positive/negative) and arousal (low/high). Additionally, Separate datasets were constructed using IAPS ratings (Instances were labeled by the corresponding image category) and self reports of subjects. In total there were 80 (4 subjects x 5 recording session’s x 2 affective measures (valence and arousal) x 2 ratings (IAPS ratings and self reports)) datasets with 80 instances in each data set.

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