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 complaints 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 provided including three invited talks have been conscientiously reviewed and chosen from 196 submissions. The papers are equipped in topical sections on attractiveness and synthesis of human impact, affect-sensitive functions, methodological matters in affective computing, affective and social robotics, affective and behavioral interfaces, proper insights from psychology, affective databases, assessment and annotation tools.
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Additional resources for Affective Computing and Intelligent Interaction: 4th International Conference, ACII 2011, Memphis, TN, USA, October 9–12, 2011, Proceedings, Part I
Mathematical Biosciences 157, 303–320 (1999) 11. : Online classification of nonstationary data streams. Intell. Data Anal. 6, 129–147 (2002) 12. : Classifier Ensembles for Changing Environments. , Windeatt, T. ) MCS 2004. LNCS, vol. 3077, pp. 1–15. Springer, Heidelberg (2004) 13. : Estimation and decision fusion: A survey. Neurocomputing 71, 2650–2656 (2008) 14. : Classifier ensembles: Select real-world applications. Information Fusion 9, 4–20 (2008) 15. : An Ensemble Approach for Incremental Learning in Nonstationary Environments.
3077, pp. 1–15. Springer, Heidelberg (2004) 13. : Estimation and decision fusion: A survey. Neurocomputing 71, 2650–2656 (2008) 14. : Classifier ensembles: Select real-world applications. Information Fusion 9, 4–20 (2008) 15. : An Ensemble Approach for Incremental Learning in Nonstationary Environments. , Roli, F. ) MCS 2007. LNCS, vol. 4472, pp. 490–500. Springer, Heidelberg (2007) 16. : International affective picture system (IAPS): Technical manual and affective ratings. The Center for Research in Psychophysiology, University of Florida, Gainesville, FL (1995) 17.
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. Updating of the weights is done automatically based on incoming data, which makes this approach suitable for online applications. The pseudo code presented in Table 1 describes the major steps of the algorithm.