支持向量机在大类别数分类中的应用

研究支持向量机在大类别数分类中的应用；结合二叉决策树的基本思想提出一种基于支持向量机（support vector machine,SVM)的大类别数分类解决方法，即SVM决策树方法，对不同背景下可选用的SVM决策树的结构进行了讨论，分析了SVM决策树的特点，并对其识别错误率进行数学进行，结果表明该方法可降低平均分类错误率，对实际应用中的多类分类问题提供新的途径。

The Application of Support Vector Machine in Classifying Large Namber of Catalogs

Support vector machine is a highly performance classification method. The basic support vector machine (SVM) is for pair class problem. The principle of SVM was introduced and a classifier based on SVM for a large number catalogs was proposed. The method was named SVM decision tree. The types of SVM decision tree selected in various cases were discussed in detail, and the features of SVM decision tree were also analysed. It was proved that the mean error of classifying N catalogs with SVM decision tree is smaller.