Abstract

Support vector machine (SVM) is a popular machine-learning method. It is difficult to obtain optimal hyperplane in SVM, thus, this study is to propose a nonlinear structural twin parametric-margin support vector machine for students' English achievements analysis. English course of students is used to study the evaluation performance for students' achievements evaluation based on nonlinear structural twin parametric-margin SVM. The experimental results show that the evaluation accuracy for students' achievements of nonlinear structural twin parametric-margin SVM is higher than TSVM, FSVM or SVM. Thus, we can conclude that nonlinear structural twin parametric-margin SVM is very suitable for students' achievements analysis.

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