一种改进的LSTSVM增量学习算法  

An Improved LSTSVM Incremental Learning Algorithm

作　　者：周水生 姚丹 ZHOU Shuisheng;YAO Dan(School of Mathematics and Statistics,Xidian University,Xi＇an 710126,China)

机构地区：[1]西安电子科技大学数学与统计学院,西安710126

出　　处：《吉林大学学报（理学版）》2018年第4期909-916,共8页Journal of Jilin University:Science Edition

基　　金：国家自然科学基金(批准号:61179040;61772020)

摘　　要：基于Sherman-Morrison定理和迭代算法,提出一种改进最小二乘孪生支持向量机(SMI-ILSTSVM)的增量学习算法,解决了最小二乘孪生支持向量机(LSTSVM)不具备结构风险最小化和稀疏性的问题.实验结果表明,该算法分类精度和效率均较高,适用于含有噪声的交叉样本集分类.We proposed an improved least squares twin support vector machine（SMI-ILSTSVM）incremental learning algorithm based on Sherman-Morrison theorem and iterative algorithm.It solved the problem that least squares twin support vector machine（LSTSVM）did not have structural risk minimization and sparsity.The experimental results show that the proposed algorithm has high classification accuracy and high efficiency,and is suitable for noise-containing cross-sample set classification.

关 键 词：最小二乘孪生支持向量机 增量学习 稀疏性 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]