基于L1范数支持向量分类机的算法扰动设计  

Research on Algorithm Disturbance Design Based on L1 Norm Support Vector Classifier

作　　者：吕洪林[1] LYU Honglin(School of Information Management,Liaoning University of International Business and Economics,Dalian,Liaoning 116052,China)

机构地区：[1]辽宁对外经贸学院信息管理学院

出　　处：《平顶山学院学报》2019年第5期37-39,共3页Journal of Pingdingshan University

摘　　要：普通向量分类机算法普遍有效率低下、算法复杂的缺点,为了进一步降低计算量,提高算法效率,提出了支持向量机算法.该算法将二次规划问题转换为线性规划问题,同时可以进行优化降次.结果显示,在数据量较大的情况下可以迅速并较为准确地处理大量数据,面对较为困难的数据处理时可以实现数据的有效分类.In order to further reduce the computational burden and improve the algorithm efficiency,the support vector machine(SVM)algorithm is proposed.This algorithm transforms the quadratic programming problem into a linear programming problem,and can optimize the order reduction.The results show that large amount of data can be processed quickly and accurately,and effective data classification can be achieved in the face of difficult data processing.

关 键 词：支持向量机 线性规划 MATLAB 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]