由于传统的支持向量机(SVM)算法的核函数没有考虑训练数据自身的特点，因而相对于具体的问题来说，往往不是最优的。为了获得最优的分类结果，提出了一种基于核变换思想的支持向量机分类方法。该方法首先根据训练样本的类属信息，通过对初始核进行线性变换来间接地达到改进输入空间到输出空间的映射函数的目的，同时利用变换后的核函数来求解分类数据特征空间的超平面方程。仿真和实验结果表明, 采用此方法, 不仅可以提高系统的分类性能和降低噪声的干扰，而且可以增强分类结果的鲁棒性。

Kernel based Support Vector Machine （SVM）does not consider inner property of training data, so classification results are usually not in optimum condition. In this paper we present a new SVM classification algorithm. The proposed method alters the kernel based on the class information of the training data, with input vectors being classified by this transformed kernel. The described algorithm can improve performance of mapping function indirectly. Simulation and experiments validate that it can improve classification performance and robustness, and reduce noise.