搜索资源列表
dbProxy
- 数据库代理，基于xml，可以有效的降低对数据库的访问次数，也有利于协议的分层实现。是数据库操作与应用层接连的好的构件模式-The database proxy, based on xml, may effective reducing to the database the access, also is advantageous to the agreement lamination realization. Is the database operation and the appl
pca+lda
- 模式识别PCA+LDA的C++源代码，用于图像的主分量分析-pattern recognition PCA LDA C source code for the image of Principal Component Analysis
n_pca
- 模式识别PCA(principle component analysis)源码.matlab 格式。PCA为经典而且经常使用的算法。-pattern recognition PCA (principle component analysis) source. Matlab format. PCA to the classic and often use the algorithm.
icatoolbox
- 独立主成分分析的工具箱，是模式识别，成分分析，线性判别的重要手段。-independent Principal Component Analysis Toolbox, pattern recognition, component analysis and linear discriminant an important means.
CalcLDA
- PCA---主成分分析 LDA---线性区别分析此类实现结合两者的有缺点实现图像模式识别,其中需要有矩阵类-PCA principal component analysis --- --- LDA linear discriminant analysis combining the two to achieve such a flawed it Image is pattern recognition, which requires matrices
PCA_face_rec
- 这是基于 PCA（主成分分析法） 算法的人脸模式识别原程序；-This is based on the PCA (Principal Component Analysis) algorithm for pattern recognition of the original face;
pca
- 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
gfd
- Surface profile measurement by noncontact optical methods has been extensively studied because of its importance in automated manufacturing, component quality control, medicine, and robotics. In most of these methods a known periodic pattern,
stprtool.rar
- 统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含： 1，Analysis of linear discriminant function 2，Feature extraction: Linear Discriminant Analysis 3，Probability distribution estimation and clustering 4，Support Vector and other Kernel Machines,
Swing
- Swing组件简介 模型-视图-控制器设计模式 Swing组件都有三个要素： 内容，例如，按钮的状态（是否按下）或者文本框中的文本。 外观显示（颜色，尺寸）。 行为（对事件的反应）。 -About Swing components Model- View- Controller design pattern Swing component has three elements: content, fo
PCA.rar
- 主元分析PCA的C代码，自己花了好几天编的，对做数据挖掘和模式识别的同志们有用,PCA principal component analysis of C code that he spent a few days for the better, and to do data mining and pattern recognition useful comrades
kfcfk
- 特征的选择与提取 模式识别 统计分量 排序-Feature selection and extraction of statistical pattern recognition component to sort
prtools
- 一个强大的统计模式识别工具箱，包含高斯分类器，高斯混合模型，主成分分析，支持向量机等常见分类方法。-A powerful statistical pattern recognition toolbox, including the Gaussian classifier, Gaussian mixture model, principal component analysis, support vector machines and other common classification met
Basedonwaveletanalysisandprincipalcomponentanalysi
- 基于小波分析和主成分分析的人脸识别研究随着社会的发展，社会各个方面对快速有效的身份验证的要求日益迫切。由 于生物特征是人的内在属性，具有很强的自身稳定性和个体差异性，因此是身份 验证的理想依据。其中利用人脸特征又是最自然直接的手段，相比其他生物特征， 它具有直接、友好、方便的特点，易于为用户接受。从而，人脸识别吸引了越来 越多来自计算机视觉和信号处理等领域的关注，成为模式识别、图像处理等学科 的研究热点。-Based on wavelet analysis and princ
PCA
- 主成分分析，人脸识别，模式识别，对图像处理有点帮助-Principal component analysis, face recognition, pattern recognition, image processing for a little help
KECA
- Kernel Entropy Component Analysis,KECA方法的作者R. Jenssen自己写的MATLAB代码，文章发表在2010年5月的IEEE TPAMI上面-Kernel Entropy Component Analysis, by R. Jenssen, published in IEEE TPAMI 2010. We introduce kernel entropy component analysis (kernel ECA) as a new method
PCA
- Principal Component Analysis. Very important for pattern recognition(ie. optical character recognition) A great fundamental file for the beginner. Even those who doesn t know what is variance can start learning about OCR basics from this pdf.-Princip
progarmlab4
- The Principal component analysis, is a standard technique used for data reduction in statistical pattern recognition and signal processing A common problem in statistical pattern recognition is feature selection or feature extraction. Feature selec
principal-component-analysis
- 主成份分析在模式识别中是一种特征提取方法！-a very important technique feature extraction in pattern recognazition
pattern-identification
- 主成分分析、Fisher判别法与支持向量机在模式识别中的应用-Application of principal component analysis, Fisher discriminant analysis and support vector machine in pattern recognition