王军涛

• 1:北华航天工业学院

摘要(Abstract)：

传统二阶特征估计法在对大数据方差进行估计,预测大型教学系统中的智能大数据关键特征时,存在对多特征的智能大数据关键特征估计效果不明显,估计结果误差累计量大的问题。因此,提出大型教学系统的智能大数据关键特征估计方法,其采用Relief关键特征估计方法获取大数据特征权重,完成智能大数据特征流行学习,通过对特征权重选择后的数据空间进行无监督学习和低维嵌入,实现对多特征的智慧大数据的特征估计。基于大数据关键特征估计结果,采用滚动时间序列估计方法,通过AR(p)模型运算大数据特征的模型阶数,依据该阶数向滚动AR算法引入实时数据,解决大数据特征估计中估计结果不同步造成的累计误差问题,实现智能大数据关键特征准确预测。实验结果表明,所提方法可增强对关键特征的估计精度,对关键特征的估计效果也有所提高。

关键词(KeyWords)： 大型教学系统;智能大数据;关键特征;Relief;时间序列估计;累计误差

Abstract:

Keywords:

基金项目(Foundation): 国家高分重大专项项目(67-Y20A07-9002-16/17);; 河北省社会科学基金项目(HB16JY005)~~

作者(Author): 王军涛

DOI: 10.16652/j.issn.1004-373x.2018.12.021

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