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.