基于支持向量机高校考试测量理论的新方法  

A New Method of College Testing Measurement Theory Based on SVM

作　　者：宋思雨[1] 袁明[1] 佟晴[1] 王美侠[1] 祝丹梅[1] 

机构地区：[1]辽宁石油化工大学理学院,辽宁抚顺113001

出　　处：《辽宁石油化工大学学报》2017年第4期57-60,共4页Journal of Liaoning Petrochemical University

基　　金：2016年国家级大学生创新创业项目(201610148061);辽宁省教育厅科学研究项目(L2015309);辽宁省本科教改项目(20160193)

摘　　要：高校考试测量对选拔人才十分重要,试卷中隐性知识的量化是选拔创新性人才的关键。首先以传统的统计方法对考试结果进行分析,然后在此基础上采用支持向量机方法,通过引入多项式核函数、径向基核函数以及兼具二者优势的组合核函数训练支持向量机,并以不同因子针对显隐性知识的量化进行比较分析,得出不同的分类结果。实验结果表明,考试测量结果的合理评价需基于显性、隐性知识分析,将组合核函数应用于考试测量的优劣是行之有效的方法。College testing measurement is very important to choose talent.The quantification of tacit knowledge in test paper is the key to choose innovative talents.First of all, the traditional statistical methods are used to analyze the test results,and then to use support vector machine (SVM) method on the basis of it.It is concluded that the classification of different effects with the introduction of polynomial kernel function,radial basis kernel functions and takes both advantages of combined SVM kernel function training.and with different factors to show the quantitative comparative analysis of tacit knowledge.The experimental results show that the test results of evaluation should be based on the analysis of explicit and implicit knowledge analysis.Reasonable combination kernel function is applied to the pros and cons of the examination measurement is effective method.

关 键 词：考试测量 隐性知识 支持向量机 组合核函数 

分 类 号：G434[文化科学—教育学]