一种支持向量机的混合核函数

Mixture kernel function of support vector machines

核函数是支持向量机的核心,不同的核函数将产生不同的分类效果。由于普通核函数各有其利弊,为了得到学习能力和泛化能力较强的核函数,根据核函数的基本性质,两个核函数之和仍然是核函数,将局部核函数和全局核函数线性组合构成新的核函数——混合核函数。该核函数吸取了局部核函数和全局核函数的优点。利用混合核函数进行流程企业供应链预测实验,仿真结果验证了该核函数的有效性和正确性。

Kernel function is the key issue of Support Vector Machines （ SVM）, and different kernel functionscan produce different SVM. As the general kernel functions have their own advantages and disadvantages, to get a kernel function with stronger learning and generalization ability, a new mixture kernel function was proposed based on the fundamental of the kernel function, that the combination of the kernel functions still be a kernel function. The new kernel function had the desirable characteristics for SVM learning and generalization, and learned the advantages of global kernels and local kernels. The comparison results between the new kernel and other kernels in forecast of process supply chain experiment certify that the new kernel can achieve better performance than other kernels.