【摘要】 核主元分析具有能较好地提取非线性特征的优势;支持向量机具有的非线性映射能力,且泛化能力强。它们在分类与识别中应用时都各有自己的优点,结合核主元分析和支持向量机的特点,提出一种基于核主元分析的支持向量机识别方法,用该方法分别对ORL人脸库和iris数据集中的数据进行分类与识别,结果表明:如果根据设计好的核函数的参数,可以得到极高的识别率。更多还原

【Abstract】 Kernel Principal Component analysis(KPCA) has the advantage of extracting nonlinear features.Nonlinear mapping and generalization are the strong capabilities of Support Vector Machine(SVM).they have own advantages when they each is applied into classification and identifition.By integrating the characteristics of KPCA and SVM,a SVM recognized method based on KPCA is put forward,and is carried on the recognition to the data of the ORL person face database and the iris data concentration,the result shows that the recognized accuracy can reach 98.6% according to designed the parameters.更多还原