搜索资源列表
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基于PSO训练SVM的人脸识别
利用支持向量机在学习能力方面表现的良好性能,结合核主元分析特征提取方法,将其应用于人脸识别中,该方法在实验中表现了良好的识别性能,为人脸识别领域提供了一条新的识别途径-PSO-based SVM for face recognition training using support vector machine learning ability in the performance of good performance, combined with KPCA
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在ORL或Yale标准人脸数据库上完成模式识别任务。用PCA与基于核的PCA（KPCA）方法完成人脸图像的重构与识别试验.
-Or Yale in the ORL face database, complete the standard pattern recognition tasks. With the PCA and kernel-based PCA (KPCA) method to complete the reconstruction of face image and reco
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matlab 源码 KPCA/核PCA 可用于人脸识别-matlab source KPCA/nuclear PCA for Face Recognition
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运用KPCA方法在ORL人脸库上进行人脸识别，分类器为最近邻分类器。-KPCA method using ORL face database for face recognition, classification for the nearest neighbor classifier.
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This program with kpca, PLS, SVM to achieve face recognition, the facial expression distinguish effect is obvious, the resolution is ninety-four percent
-This program with kpca, PLS, SVM to achieve face recognition, the facial expression distinguis
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一个模式识别程序，关于KPCA/KFD的人脸识别程序，MATLAB-Pattern recognition prog, for face reconginition, MATLAB language
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核主成分分析，用于轴承故障，人面识别，水位分布等的数据非线性提取。-Kernel principal component analysis for data bearing failure, human face recognition, water distribution and other non-linear extraction.
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(1)Bayes分类
已知N=9， =3，n=2，C=3,问x= 应属于哪一类？
(2)聚类
使用c－均值聚类算法在IRIS数据上进行聚类分析
(3)鉴别分析
在ORL或Yale标准人脸数据库上完成模式识别任务。
用PCA与基于核的PCA（KPCA）方法完成人脸图像的重构与识别试验。-(1) Bayes classification
Known N = 9, = 3, n = 2, C = 3, x = should ask which cat
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非常有用的人脸识别工具包，包含了各种PCA,LDA, KPCA, KFA及其变种。在Matlab环境下可以直接运行。-The PhD (Pretty helpful Development functions for) face recognition toolbox is a collection of Matlab functions and scr ipts intended to help researchers working in the field of face recognit
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MATLAB人脸识别（PCA,LDA,KPCA,BP，可视化界面，摄像头）(Matlab face recognition (PCA, LDA, KPCA, BP, visual interface, camera))
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