互联网环境下基于改进支持向量机的高校教学评价研究

Relation Extraction Based on Directed Acyclic Graph-Support Vector Machines

教学评价是高校教学中非常重要的一环,一般的多分类SVM会存在不可分区域,用其来进行教学评价,会影响教学评价的效果。为提高高校教学评价准确性,结合高校课堂教学的实际情况,提出了一种改进支持向量机的高校教学评价方法,并且引入DAG-SVM多分类方法和FMSVM多分类方法来解决不可分区域问题。通过用一般的多分类方法、FMSVM多分类和DAG-SVM多分类方法进行实验比较。结果表明,文章方法对教学评价的准确性有一定的提高。

Teaching evaluation is a very important part of College teaching.Generally,multi-classification SVM can not be divided into regions.Using it to evaluate teaching will affect the effect of teaching evaluation.In order to improve the accuracy of teaching evaluation in Colleges and universities,combined with the actual situation of classroom teaching in Colleges and universities,this paper proposes an improved teaching evaluation method of support vector machine in Colleges and universities,and introduces DAG-SVM multi-classification method and FMSVM multi-classification method to solve non-subregional problems.Experiments are carried out to compare the general multi-classification method,FMSVM multi-classification method and DAG-SVM multi-classification method.The results show that the accuracy of teaching evaluation has been improved.