基于偏最小二乘的支持向量机多分类方法

该文提出了一种基于偏最小二乘（PLS）的支持向量机（SVM）多分类方法，该算法利用偏最小二乘思想对样本进行预处理，消除了样本属性之间的相关性，而且得到的综合属性与类信息的相关程度达到最大。通过实验可以看出，该方法不仅可以减少用支持向量机进行分类过程中的支持向量数目，而且当样本属性较多时，可以提高一定的识别率。

Support Vector Machine multi-classified method based on partial least-squares

The paper proposes a multi-classified method of Support Vector Machine（SVM） based on Partial Least Squares（PLS）.The partial least squares is used to complete the pretreatment of the sample，it can eliminate the correlation of the samples’ attribute，and it can also maximize the correlation between the general components and the information of kinds.Through the result of the experiment，we can see that the method can not only reduce the amount of the support vectors in the process of the classification with support vector machine，but also heighten the accuracy ratio of the recognition when the amount of the samples’ attribute is larger.