Title :
Research of improved SVM model based on GA in E-learning emotion classification
Author :
Wansen Wang ; Wenlan Ding
Author_Institution :
Capital Normal Univ. of Eng. Intell., Beijing, China
fDate :
Oct. 30 2012-Nov. 1 2012
Abstract :
Aiming at the problem of different learning emotions in the integrated emotion model of the emotion recognition interactive E-learning system, this paper proposed an improved SVM classification algorithm to classify the learning emotions, optimized the parameters of the SVM classifier with Genetic Algorithm, and applied it to the emotion classification of E-learning system. It has achieved good results based on relevant experiments.
Keywords :
computer aided instruction; emotion recognition; genetic algorithms; pattern classification; support vector machines; GA; SVM classification algorithm; e-learning emotion classification; emotion recognition interactive E-learning system; genetic algorithm; improved SVM model; integrated emotion model; Accuracy; Analytical models; Electronic learning; Genetic algorithms; Support vector machines; Training; E-learning; genetic algorithm; learning emotion classification; support vector machine;
Conference_Titel :
Cloud Computing and Intelligent Systems (CCIS), 2012 IEEE 2nd International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4673-1855-6
DOI :
10.1109/CCIS.2012.6664310
