支持向量机理论与应用  

Theory and Application of Support vector machine

作　　者：刘铭[1] 吴朝霞[1] LIU Ming;WU Zhao-xia(School of Computer Science and Information Engineering Anyang Institute of Technology,Anyang Henan 455000,China)

机构地区：[1]安阳工学院计算机科学与信息工程学院,河南安阳455000

出　　处：《科技视界》2018年第23期68-69,共2页Science & Technology Vision

摘　　要：支持向量机(support vector machine, SVM)是基于统计学理论的一种典型的机器学习方法,具有坚实的理论基础,较强的推广能力,在解决小样本、非线性、高维度的问题上SVM表现出较好的学习性能。随着研究的深入, SVM被广泛应用于各个领域,本文介绍了统计学和支持向量机的基本理论,支持向量机的相关应用研究及未来研究方向和发展前景。Support vector machine （SVM）is a typical machine learning method based on statistical theory, It has a solid theoretical foundation and strong promotion ability. SVM Shows excellent performance on small sample,nonlinear and high dimensional problems. With the deepening of the research,SVM is widely used in various fields. This paper introduces the basic theoty of statistics and SVM,the related application research of SVM and future research directions and development prospects.

关 键 词：支持向量机 统计学习理论 应用 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]