Exploring online intelligent teaching method with machine learning and SVM algorithm

In order to improve the effect of modern music teaching, this paper builds an intelligent music teaching system based on machine learning and SVM algorithm, innovates the music teaching process, and gradually expands from the simplest three-layer structure BPNN to a multi-layer structure. Moreover,...

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Bibliographic Details
Published in:Neural computing & applications 2022-02, Vol.34 (4), p.2583-2596
Main Authors: Shuo, Wang, Ming, Mu
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
CAI
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer assisted instruction
Computer Science
Data Mining and Knowledge Discovery
Errors
Image Processing and Computer Vision
Machine learning
Multilayers
Music teachers
Probability and Statistics in Computer Science
S.I: Cognitive-inspired Computing and Applications
Special Issue on Cognitive-inspired Computing and Applications
Support vector machines
Teaching
Teaching machines
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Summary:In order to improve the effect of modern music teaching, this paper builds an intelligent music teaching system based on machine learning and SVM algorithm, innovates the music teaching process, and gradually expands from the simplest three-layer structure BPNN to a multi-layer structure. Moreover, this paper proposes a method of dividing the error by the proportion of each link's contribution to the error, and the idea that the error of the hidden layer node is the sum of the errors on each link during the forward propagation process. In addition, this paper combines the actual needs of music teaching to construct an intelligent music teaching system. Finally, this paper conducts training tests on music teaching data and sets up the experimental group and the control group to evaluate the system teaching effect based on actual needs. The research results show that the performance of the intelligent music teaching system constructed in this paper is good.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-021-05846-6