支持向量机最优参数选择的研究

支持向量机是近几年发展起来的机器学习方法，模型选择是设计支持向量机的关键。基于高斯核函数的支持向量机具有良好的学习性能，被广泛应用于模式分类中，讨论了核函数中C和y对分类函数的影响，提出针对不同类型的数据，SVM应选用不同的核函数，同时利用二分法对核函数（C，γ）寻优，并将其应用于变压器故障诊断中，仿真结果表明该方法具有较好的性能。

Study on the choice optimum parameters of support vector machine

Support vector machine(SVM) is a novel machine learning method.Model selection is essential to design SVM.The support vector machine based on Gauss kernel function is widely used in model classification due to its good properties.This paper studied the influences of the error penalty parameter and the kernel parameter on support vector machine's generalization ability and the method of the choice of the optimum parameter at present.At the same time,this paper presented that SVM should choose different kerne...