Firstly KPCA is used to extract the features of human face image, and then SVM combined with the nearest distance rule is used for classification, which depends on the kernel principal components.
该方法首先利用核主元分析对人脸图像进行特征提取，然后依据支持向量机与最近邻准则对所提取的核主元特征进行分类识别。
The experiment results concludes that the SVM classification method based on KPCA have the better classification effect than the other three.
实验结果表明，基于KPCA特征提取法的支持向量机分类器的分类错误率在这四种分类算法中最低。
The dissertation mainly aims at applying support vector machine (SVM) and kernel principal component analysis (KPCA) to intrusion detection.
本文的主要工作是将支持向量机(SVM)及核主成分分析(KPCA)应用到入侵检测技术中。
By integrating the characteristics of KPCA and SVM, a face recognition method based on these two algorithms is presented.
结合核主元分析与支持向量机的特点，提出了一种基于核主元分析与支持向量机的人脸识别方法。
By integrating the characteristics of KPCA and SVM, a face recognition method based on these two algorithms is presented.
结合核主元分析与支持向量机的特点，提出了一种基于核主元分析与支持向量机的人脸识别方法。
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