支持向量机在分类中的应用  

Applications of Support Vector Machines in Classification

作　　者：陆波[1] 尉询楷[1] 毕笃彦[1] 

机构地区：[1]空军工程大学工程学院,西安710038

出　　处：《中国图象图形学报》2005年第8期1029-1035,共7页Journal of Image and Graphics

摘　　要：通过引入结构风险最小化原则和最优分类面的概念,介绍了支持向量机及其用于非线性分类的基本原理和训练算法,并选用不同的核函数及参数对一组线性不可分的两类样本进行了划分识别,得到了较好的效果,并对结果进行了分析说明,展望了支持向量机的发展趋势。This paper introduces the theory and training algorithm of the support vector machine which is applied in nonlinear classification and recognition by the way of bringing in the concept such as structural risk minimization principle and optimal hyperplane, then a set of nonlinear binary samples are successfully classified by using different kernel functions, followed by discussion to the results. After that current multi-class classification algorithms and application areas are reviewed. Finally future developments are prospected.

关 键 词：支持向量机 最优分类面 非线性分类 二次规划 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]