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Based on the SVM University Education's Quality Regression Analysis

机译:基于支持向量机的大学教育质量回归分析

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Due to the complexity of the quality control of higher education and its influence factors, it has always been difficult to have a control on the quality of higher education so as to realize the quantification analysis and give a prediction for the future quality. The ordinary ways of regression analysis have difficulty in establishing models and may lead to ȁC;over learningȁD;. The support vector machine (SVM) does not have a strict requirement on the number of samples, the distribution of process errors and sample points, and is easy to promote. In this paper, We make a SVM regression analysis of the quality control and prediction of higher education and put forward a regression model with strong generalization ability from the angle of machine learning. The results of the effect of fitting are good under the Kolmogorov-Smirnov (KS) test. Thus, the problems of establishing models, making quantification analysis in the quality control of higher education can have a solution
机译:由于高等教育质量控制的复杂性及其影响因素,一直很难对高等教育质量进行控制,以实现量化分析和对未来的质量进行预测。回归分析的常规方法难以建立模型,并可能导致ȁC;过度学习ȁD。支持向量机(SVM)对样本数量,过程误差和样本点的分布没有严格要求,并且易于推广。在本文中,我们对高等教育的质量控制和预测进行了SVM回归分析,并从机器学习的角度提出了具有较强泛化能力的回归模型。在Kolmogorov-Smirnov(KS)测试中,拟合效果的结果很好。因此,在高等教育质量控制中建立模型,进行量化分析的问题可以得到解决。

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