Open AccessApplication of slack variable-optimized SVM on piano teaching reform
Author(s) -
Tianjiao Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1941/1/012084
Subject(s) - support vector machine , computer science , task (project management) , machine learning , artificial intelligence , sample (material) , quality (philosophy) , variety (cybernetics) , variable (mathematics) , pattern recognition (psychology) , data mining , mathematics , engineering , mathematical analysis , philosophy , chemistry , systems engineering , epistemology , chromatography
The data mining task of the classification algorithm is mainly to classify the data and classify them into each known category. As a classification algorithm, SVM has many unique advantages in solving small sample, nonlinear and high-dimensional pattern recognition. Based on the advantages of the SVM algorithm, this paper optimizes the slack variables, establishes a teaching quality evaluation model, and takes 500 subjects of the questionnaire survey as data samples by comparing with a variety of classification methods to evluate, and the results show that the accuracy of the SVM model is 92.70%, which proves the effectiveness of the research model.