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    题名: Learning collaborative decision-making parameters for multimodal emotion recognition
    作者: Huang, K.-C.;Lin, H.-Y.S.;Chan, J.-C.;Kuo, Yau-Hwang
    郭耀煌
    贡献者: 資科系
    关键词: Affective communication;Classification methods;Collaborative decision making;Emotion recognition;Genetic learning;Local minima problems;Multimodal emotion recognition;Optimal solutions;Exhibitions;Decision making
    日期: 2013-07
    上传时间: 2015-05-26 18:28:14 (UTC+8)
    摘要: In this paper, we present a novel multimodal emotion recognition technique that automatically learns decision-making parameters customized for each modality. Specifically, the process of decision-making is implemented in a multi-stage and collaborative fashion: Given a classifier for single modality, the classifier is regarded as a virtual expert since classification methods can make emotion recognition in accordance with certain expertise. Then, in the reputation equalization, the expert`s classification capability is then quantitatively equalized to assure the reputation and/or confidence for each expert. To compromise decisions among experts, the final decision is obtained by calculating the weighted-sum of all the equalized reputation quantities, in such a way that the decision of one expert can be made in collaboration with that of the others. Moreover, to learn the proposed model parameters, the genetic algorithm is tailored and applied to alleviate the local minima problem during the process of finding an optimal solution. The experimental results have shown that the proposed collaborative decision-making model is effective in multimodal emotion recognition. © 2013 IEEE.
    關聯: Proceedings - IEEE International Conference on Multimedia and Expo, 2013, 論文編號 6607472, 2013 IEEE International Conference on Multimedia and Expo, ICME 2013; San Jose, CA; United States; 15 July 2013 到 19 July 2013; 類別編號CFP13ICM-ART; 代碼 100169
    数据类型: conference
    DOI 連結: http://dx.doi.org/10.1109/ICME.2013.6607472
    DOI: 10.1109/ICME.2013.6607472
    显示于类别:[資訊科學系] 會議論文

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