核函数是支持向量机(SVM)的核心，直接决定着 SVM 的性能． 为提高 SVM 在语音识别问题中的学习能力和泛化能力，文中提出了一种 Logistic 核函数，并给出了该Logistic核函数是 Mercer 核的理论证明． 在双螺旋、语音识别问题上的实验结果表明，该Logistic 核函数是有效的，其性能优于线性、多项式、径向基、指数径向基的核函数，尤其是在语音识别中，该 Logistic 核函数具有更好的识别性能．

Kernel function is the core of support vector machine (SVM) and directly affects the performance of SVM． In order to improve the learning ability and generalization ability of SVM for speech recognition，a Logistic kernel function，which is proved to be a Mercer kernel function，is presented． Experimental results on bi-spiral and speech recognition problems show that the presented Logistic kernel function is effective and performs better than linear，polynomial，radial basis and exponential radial basis kernel functions，especially in the case of speech rec-ognition．