Abstract
Aiming at the problem of poor recognition accuracy and poor recognition sensitivity caused by the poor feature extraction effect of the traditional talent training pattern recognition model. This paper proposes a method to identify the training pattern of Japanese talents in colleges and universities in online education. The principle of separability is used in the process of extracting the target feature quantity. Based on the extracted feature wavelet neural network architecture is constructed. The parameters of the neural network are trained to obtain the optimal parameters. Construct a talent training pattern recognition model through optimal parameters to realize the recognition of talent training patterns. The experimental results show that the accuracy of the recognition method is 92.93% on average, and it can obtain better talent training effect in application.
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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Sun, H., Liu, Y. (2021). Pattern Recognition Method of Training Japanese Talents in Online Education. In: Fu, W., Liu, S., Dai, J. (eds) e-Learning, e-Education, and Online Training. eLEOT 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-030-84383-0_21
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DOI: https://doi.org/10.1007/978-3-030-84383-0_21
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