文献类型： 会议论文
第一作者： Anis Elbahi
作者： Anis Elbahi 1 ; Mohamed Nazih Omri 1 ; Mohamed Ali Mahjoub 2 ;
作者机构： 1.Research Unit MARS, Department of computer sciences, Faculty of sciences of Monastir
2.Research Unit SAGE, National Engineering School of Sousse
关键词： Hidden Markov Models;Possibilistic Theory;Fuzzy Logic;Human Activity Recognition
会议名称： IEEE International Conference on Fuzzy Systems
主办单位：
页码： 1762-1769
摘要： Hidden Markov Models (HMM) have been widely used in classification tasks. Despite their efficiency in stochastic sequences labeling, they are overwhelmed by imperfect quality of used data in the learning and inference processes. In this paper, we try to evaluate the contribution of possibilistic theory in creating sequences of observations used by HMM models. Experimental results show that observation sequences, obtained by possibilistic reasoning significantly, improve the performance of HMM in the recognition of online e-learning activities.
分类号： TP273.4-53
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