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【Graph Recognize】 PCA+LDA.Class.vc DL : 0: 结合PCA+LDA的图像识别算法VC封装类,PCA（主元素分析，光照敏感）,可用于人脸识别的初级算法-combination of image recognition algorithm VC Packaging category, PCA (principal component analysis, Light-sensitive), can be used for the initial face recognition algorithm
Update : 2008-10-13 Size : 3593 Publisher : 郑翠花
【Other resource】 pca DL : 0: 应用PCA（主成分分析）进行人脸识别的matlab程序，有较高成功率-PCA (Principal Component Analysis) face recognition Matlab procedures, which have a higher success rate
Update : 2008-10-13 Size : 1448 Publisher : li
【Special Effects】 PixelFusion DL : 0: 利用vc实现图像的融合,包括一些经典的图像融合算法和主成分分析实现图像的融合.-use vc image fusion including some classic image fusion algorithms and principal component analysis image integration.
Update : 2008-10-13 Size : 5093507 Publisher : 张素兰
【Other resource】 PCA(matlab) DL : 0: 主成分分析算法（PCA），这是一个外国人编写的，很具有参考价值-principal component analysis algorithm (PCA), which was prepared by a foreigner, it is very valuable reference
Update : 2008-10-13 Size : 33277 Publisher : 王超
【Special Effects】 pca DL : 0: 这是一个模式识别中关于主成分分析的特征提取的matlab源码-This is a pattern recognition on the Principal Component Analysis Feature Extraction of Matlab FOSS
Update : 2008-10-13 Size : 59698 Publisher : fuali
【GDI-Bitmap】 principal_components DL : 0: PCA = Principal Component Analysis PDF document. Learn it
Update : 2008-10-13 Size : 91008 Publisher : selamicik
【Other resource】 IntroMatlab_codepls DL : 0: PCA and PLS aims:to get some insight into the bilinear factor models Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression, focusing on the mathematics and numerical aspects rather than how s and why s of data analysis practice. For the latter part it is assumed (but not absolutely necessary) that the reader is already familiar with these methods. It also assumes you have had some preliminary experience with linear/matrix algebra.
Update : 2008-10-13 Size : 270941 Publisher : 郭大
【Other resource】 CoreJSP DL : 0: Core JSP In recent years, a large amount of software development activity has migrated from the client to the server. The client-centric model, in which a client executes complex programs to visualize and manipulate data, is no longer considered appropriate for the majority of enterprise applications. The principal reason is deployment—it is a significant hassle to deploy client programs onto a large number of desktops, and to redeploy them whenever the application changes. Instead, applications are redesigned to use a web browser as a \"terminal\". The application itself resides on the server, formatting data for the user as web pages and processing the responses that the user fills into web forms.
Update : 2008-10-13 Size : 1965127 Publisher : gyzhen
【Other】 palmprotected DL : 0: In the field of biometrics, palmprint is a novel but promising technology. Limited work has been reported on palmprint identification and verification, despite the importance of palmprint features. There are many unique features in a palmprint image that can be used for personal identification. Principal lines, wrinkles, ridges, minutiae points, singular points, and texture are regarded as useful features for palmprint representation.
Update : 2008-10-13 Size : 154321 Publisher : Frankie
【Audio program】 audio_process_demo DL : 0: Garbe.Sound is a small .NET library for audio processing. The principal idea is to give programmers with a RAD environment for developing audio filters. With the basic classes, it comes with a few filters already implemented
Update : 2008-10-13 Size : 20786 Publisher : 政逸
【WEB Code】 ATutorialonPrincipalComponentAnalysis DL : 0: A Tutorial on Principal Component Analysis.Principal component analysis (PCA) is a mainstay of modern data analysis - a black box that is widely used but poorly understood. The goal of this paper is to dispel the magic behind this black box.
Update : 2008-10-13 Size : 299986 Publisher : viv
【Other resource】 ppca DL : 0: Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the principal ％ component subspace U of dimension PPCA_DIM using a centred covariance matrix X. The variable VAR contains the off-subspace variance (which is assumed to be spherical), while the vector LAMBDA contains the variances of each of the principal components. This is computed using the eigenvalue and eigenvector decomposition of X.
Update : 2008-10-13 Size : 1268 Publisher : 西晃云
【Other resource】 pca DL : 0: pca人脸识别This package implements basic Principal Component Analysis in Matlab and tests is with grayscale portion of the FERET database. Images are not preprocessed and it is up to the user to preprocess the images as wanted, not changing the filenames
Update : 2008-10-13 Size : 2636 Publisher : 蔡加欣
【Other resource】 trainList DL : 0: PCA人脸识别 This package implements basic Principal Component Analysis in Matlab and tests is with grayscale portion of the FERET database. Images are not preprocessed and it is up to the user to preprocess the images as wanted, not changing the filenames
Update : 2008-10-13 Size : 3317 Publisher : 蔡加欣
【Other resource】 feret DL : 0: PCA人脸识别 This package implements basic Principal Component Analysis in Matlab and tests is with grayscale portion of the FERET database. Images are not preprocessed and it is up to the user to preprocess the images as wanted, not changing the filenames
Update : 2008-10-13 Size : 2353 Publisher : 蔡加欣
【Other resource】 fc DL : 0: PCA人脸识别 This package implements basic Principal Component Analysis in Matlab and tests is with grayscale portion of the FERET database. Images are not preprocessed and it is up to the user to preprocess the images as wanted, not changing the filenames
Update : 2008-10-13 Size : 967 Publisher : 蔡加欣
【Other resource】 createDistMat DL : 0: PCA人脸识别 This package implements basic Principal Component Analysis in Matlab and tests is with grayscale portion of the FERET database. Images are not preprocessed and it is up to the user to preprocess the images as wanted, not changing the filenames
Update : 2008-10-13 Size : 1480 Publisher : 蔡加欣
【assembly language】 2Dcrystals DL : 0: Negative principal refractive indices and accidental isotropy in two-dimensional photonic crystals with an asymmetrical unit ce
Update : 2008-10-13 Size : 597059 Publisher : Qtai
【Other resource】 pca DL : 1: matlab principal component analysis, PCA算法.
Update : 2008-10-13 Size : 1707 Publisher : guodong
【Other resource】 progmfc2 DL : 0: The production of this book required the efforts of many people, but two in particular deserve to be singled out for their diligent, sustained, and unselfish efforts. Sally Stickney, the book s principal editor, navigated me through that minefield called the English language and contributed greatly to the book s readability. Marc Young, whose talents as a technical editor are nothing short of amazing, was relentless in tracking down bugs, testing sample code, and verifying facts. Sally, Marc: This book is immeasurably better because of you. Thanks.
Update : 2008-10-13 Size : 5461675 Publisher : ch

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