计算机工程与应用2019，Vol.55Issue(3)：10-14,5.DOI:10.3778/j.issn.1002-8331.1808-0300
稀疏结构化最小二乘双支持向量回归机
Sparse Structured Least Squares Twin Support Vector Regression Machine
摘要
Abstract
The Least Squares Twin Support Vector Regression(LSTSVR)machine simplifies the quadratic programming problem in the Twin Support Vector Regression(TSVR)machine to the solution of two linear equations by introducing the least squares loss, thus greatly reducing the training time. However, LSTSVR minimizes the empirical risk based on least squares loss, which will lead to the following shortcomings:(1)the problem of"over-learning";(2)the solution of model lacks sparsity and it is difficult to train large-scale data. For(1), the Structured Least Squares Twin Support Vector Regression(S-LSTSVR)is given to improve the generalization ability of the model. For(2), the low rank approximation is carried out to the kernel matrix by using incomplete Choesky decomposition, and an sparse algorithm is given for solving S-LSTSVR mode(l SS-LSTSVR), which makes the model train large-scale data effectively. Experiments on artificial data and UCI data sets show that SS-LSTSVR can avoid"over learning"and can solve large-scale training problems efficiently.关键词
最小二乘双支持向量回归/结构风险最小化/稀疏性/不完全Choesky分解/大规模Key words
Least Squares Twin Support Vector Regression(LSTSVR)/structural risk minimization/sparsity/incom-plete Choesky decomposition/large-scale分类
信息技术与安全科学引用本文复制引用
闫丽萍,马家军,陈文兴..稀疏结构化最小二乘双支持向量回归机[J].计算机工程与应用,2019,55(3):10-14,5.基金项目
国家自然科学基金(No.41671165,No.61650201) （No.41671165,No.61650201）
北京市自然科学基金(No.4162058) （No.4162058）
北京未来芯片技术高精尖创新中心科研基金(No.KYJJ2018004) （No.KYJJ2018004）
北京市属高校高水平教师队伍建设支持计划高水平创新团队建设计划项目(No. IDHT20180515). （No. IDHT20180515）
