Abstract
Aiming at the problem of low accuracy of pattern recognition in online English education, a pattern recognition method based on feature self-learning is proposed. Using feature self-learning algorithm and convolution neural network, the feature extraction model of online English education is established, and the basic features and depth features of online English education are extracted. Z transform is introduced to standardize the feature data set of English online education, and Laplace gradient function is used to clean the wrong and missing data of English online education features. The fuzzy logic theory is used to infer the important parameters of English online education. According to the determined characteristics and parameter values of English online education, the difference between the characteristics and parameter values of English online education is calculated, and the mode of English online education is identified. The experimental results show that: This study of English online education pattern recognition method, can identify all the patterns of English online education, and English online education pattern recognition accuracy is high.
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References
Cai, Y., Wang, Z.: The research method of pattern recognition framework based on the distribution. J. Shandong Agric. Univ. (Nat. Sci. Ed.) 51(2), 266–268 (2020)
Chen, L.: Application of artificial intelligence in pattern recognition. Sci. Technol. Innov. Herald 17(19), 125–127 (2020)
Lu, H.: Machine vision pattern recognition method research. Mod. Inf. Technol. 4(11), 83–85 (2020)
Mou, Z., Zhang, Q., Chen, S.: Learning behavior pattern recognition and teaching intervention based on behavior sequence. Mod. Educ. Technol. 30(5), 74–80 (2020)
Guo, H., Zhao, J., Liu, X., et al.: Comprehensive design experiment for pattern recognition based on Python. Exp. Technol. Manage. 36(8), 178–181 (2019)
Xu, X.: Trajectory distribution pattern adaptive recognition simulation based on matching algorithm. Comput. Simul. 36(11), 410–413,440 (2019)
Zhou, B., Xie, G.: Research on key technologies of artificial intelligence in pattern recognition. Mod. Inf. Technol. 3(22), 110–111 (2019)
Liu, H., Hou, S.: Research and practice on bilingual teaching of postgraduate pattern recognition course. J. Anhui Univ. Technol. (Soc. Sci. Ed.) 36(1), 67–68 (2019)
Zhang, Z., Yang, C.: Identifying two refactoring patterns based on hunk and abstract syntax tree. Intell. Comput. Appl. 9(3), 146–150 (2019)
Wang, H., Liu, G., Duan, J., et al.: Transportation mode detection based on self-learning of features. J. Harbin Eng. Univ. 40(2), 354–358 (2019)
Liu, S., Li, Z., Zhang, Y., et al.: Introduction of key problems in long-distance learning and training. Mob. Netw. Appl. 24(1), 1–4 (2019)
Fu, W., Liu, S., Srivastava, G.: Optimization of big data scheduling in social networks. Entropy 21(9), 902 (2019)
Liu, S., Lu, M., Li, H., et al.: Prediction of gene expression patterns with generalized linear regression model. Front. Genet. 10, 120 (2019)
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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Duan, Xx., Duan, P. (2021). Research on Pattern Recognition Method of Online English Education Based on Feature Self Learning. 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_23
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DOI: https://doi.org/10.1007/978-3-030-84383-0_23
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