一种非线性支持向量机决策树多值分类器

提出了一种非线性支持向量机决策树的分类算法．该算法通过核函数将支持向量机推广到非线性支持向量机，并在非线性映射后计算特征空间中类间相对分离度，得到类的易分程度．在支持向量机决策树分类中引入相对分离度，有效地降低累积误差，减少计算规模，从而提高分类精度与分类效率．实验结果表明，与一般的线性支持向量机决策树分类算法相比，该算法的分类精度有了明显提高，同时其分类时间也相应降低．

Multi-class classifier of non-linear SVM decision tree

A classification algorithm of non-linear support vector machine(SVM) decision tree is presented.The presented method extends the SVM to the non-linear SVM by using kernel functions and calculates the relativity separability measure between classes after non-linear mapping.This method is introduced on the basis of the SVM decision tree.As a result,the iterative error is effectively restrained and the efficiency improved accordingly.Experimental results have shown that,compared with the original SVM decision tree algorithm,the classification rate has increased greatly and that the classification time decreased apparently.