Prediction of English teachers’ professional development based on data mining and time series model

[1] Shi, Z. (2011). Advanced Artificial Intelligence. Beijing: Science Press. Search in Google Scholar

[2] Goodfellow, I., Bengio, Y., Courville, A., et al. (2016). Deep learning (Vol. 1). Cambridge: MIT Press. Search in Google Scholar

[3] Qian, Z., & Sun, D. (2013). Application of Data Mining in Graduate Education Management Information System. Degrees and Graduate Education, 5, 46-49. Search in Google Scholar

[4] Zhang, Y. (2017). Innovative development path of educational management informationization in colleges and universities in the era of big data. Journal of Jiaozuo University, 12(04), 117-119. Search in Google Scholar

[5] Yang, C. (2018). The employment situation and countermeasures of college students under the background of the new era. Modern Enterprise, 399(12), 99-100. Search in Google Scholar

[6] Xie, H., Feng, G., & He, W. (2018). Research on Semantic Classification of Scientific and Technological Documents Based on Deep Learning. Information Theory and Practice, 41(11), 149-154. Search in Google Scholar

[7] Cai, L., & Ma, H. (2012). The application of data mining technology in college employment forecast analysis. Microcomputer Information, 28(8), 101-103. Search in Google Scholar

[8] Qin, Y., & Gu, P. (2014). Application analysis of Apriori algorithm in employment prediction of college graduates. Light Industry Science and Technology, (7), 93-94. Search in Google Scholar

[9] Wang, S. (2014). The application of C4.5 classification algorithm in higher vocational employment prediction. Modern Computer (Professional Edition), (23), 21-25. Search in Google Scholar

[10] Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36(4), 193-202. Search in Google Scholar

[11] Yu, K., Jia, L., Chen, Y., et al. (2013). Yesterday, today and tomorrow of deep learning. In Artificial Intelligence Conference of China Computer Society 2013 (pp. 1799-1804). Search in Google Scholar

[12] Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504-507. Search in Google Scholar

[13] Wang, T., Zheng, N., & Yuan, Z. (2002). Statistical learning methods in the research of machine intelligence and pattern recognition. Acta Automatica Sinica, 28(Supplement), 103-116. Search in Google Scholar

[14] Sun, D., Liu, F., & Dan, B. (1998). Study on extensive quality Loss function model. Chinese Journal of Mechanical Engineering, 34, 26-32. Search in Google Scholar

[15] Zhai, Y., Han, P., Wang, D., et al. (2003). SVM algorithm based on loss function and its application in minor fault diagnosis. Proceedings of the Chinese Society of Electrical Engineering, (09), 198-203 Search in Google Scholar