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An Empirical Study on the Application of Machine Learning for Higher Education and Social Service

An Empirical Study on the Application of Machine Learning for Higher Education and Social Service

Bingqing Yang
Copyright: © 2022 |Volume: 30 |Issue: 7 |Pages: 16
ISSN: 1062-7375|EISSN: 1533-7995|EISBN13: 9781668435700|DOI: 10.4018/JGIM.296723
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MLA

Yang, Bingqing. "An Empirical Study on the Application of Machine Learning for Higher Education and Social Service." JGIM vol.30, no.7 2022: pp.1-16. http://doi.org/10.4018/JGIM.296723

APA

Yang, B. (2022). An Empirical Study on the Application of Machine Learning for Higher Education and Social Service. Journal of Global Information Management (JGIM), 30(7), 1-16. http://doi.org/10.4018/JGIM.296723

Chicago

Yang, Bingqing. "An Empirical Study on the Application of Machine Learning for Higher Education and Social Service," Journal of Global Information Management (JGIM) 30, no.7: 1-16. http://doi.org/10.4018/JGIM.296723

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Abstract

The work used the current mature computer technology, machine learning technology, and other high-tech to explore the comprehensive application of educational information management under the Internet to provide educational scientific researchers with a retrieval platform for educational statistical information. Deep learning was used to extract useful network features more effectively and make the machine learning model fully consider the constraints of satisfying the constraints and optimization objectives in the problem. Based on the classification of the restricted Boltzmann machine, the Gauss-binary conditional classification of the restricted Boltzmann machine model was proposed as the routing decision unit, with the given specific training algorithm of the model.