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用MATLAB实现如何用判别分析法确定最佳阈值-MATLAB how discriminant analysis method to determine the optimal threshold value
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本程序采用线性判别分析方法实现的字符识别,达到较高的识别率。并给出了很多实验得到的识别结果-the adoption of linear discriminant analysis method of character recognition, reach a higher recognition rate. Given the many experimental results Recognition
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利用fisher线性判别分析进行数据降维-using linear discriminant analysis of data dimensionality reduction
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核化的直接判别分析(KDLDA)代码,MATLAB写的。-nuclear direct discriminant analysis (KDLDA) code, written in MATLAB.
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通过核来泛化的判别分析(GDA)代码,MATLAB写的。-through nuclear generalization to the discriminant analysis (GDA) code, written in MATLAB.
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Feature scaling for kernel Fisher discriminant analysis using leave-one-out cross validation.
FS-KFDA is a package for implementing feature scaling for kernel fisher discriminant analysis.-Feature scaling for kernel Fisher discrim inant analysis us
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这里面包含两个文件,可以实现主成分分析(PCA)算法和线性判别分析(LDA)算法。不过还需要CMatrix类的定义。-which contained two documents, can be achieved principal component analysis (PCA) algorithm and linear discriminant analysis (LDA) algorithm. But also need CMatrix class definition.
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现有的代数特征的抽取方法绝大多数采用一维的方法,即首先将图像转换为一维向量,再用主分量分析(PCA),Fisher线性鉴别分析(LDA),Fisherfaces式核主分量分析(KPCA)等方法抽取特征,然后用适合的分类器分类。针对一维方法维数过高,计算量大,协方差矩阵常常是奇异矩阵等不足,提出了二维的图像特征抽取方法,计算量小,协方差矩阵一般是可逆的,且识别率较高。-existing algebra feature extraction method using a majority of th
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正交线性判别分析(Orthogonal Linear Discriminant Analysis),可以用于数据降维上面。-orthogonal linear discriminant analysis (Orthogonal Linear Discriminan t Analysis), can be used for cutting down the data above.
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基于奇异值分解的核线性判别分析(Kernel Discriminant Analysis via QR Decomposition)。
-based on the singular value decomposition of the nuclear linear discriminant analysis (Kernel Discriminant An alysis via QR Decomposition).
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LDA/GSVD算法matlab代码,可用来实现基于广义奇异值分解的线性鉴别分析方法-LDA / GSVD algorithm Matlab code, can be used to achieve based on the generalized singular value decomposition of linear discriminant analysis method
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ldatrace performs Linear Discriminant Analysis with optional constraints on the transform matrix
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MATLAB是一款优秀的数学计算软件,在各工程领域中应用都十分广泛。本书以MATLAB R2011a为基础,在第1版的基础上进一步介绍了MATLAB的概率与数理统计分析。本书以概率与数理统计的基本原理为线索,以MATLAB为辅助条件,分别介绍了概率与数理统计概述、统计估计、假设检验、方差分析、回归分析、正交试验分析、聚类分析、判别分析和多元数据相关分析等内容,理论与实践相结合,向读者演示了MATLAB在数理统计中的应用。-MATLAB is a mathematical calculation
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代码基于Matlab软件,讲述了大量数学方法的应用思路和过程。内容涉及回归分析、主成分分析、因子分析、聚类分析、判别分析、时(空)间序列分析、Markov链、R/S分析、线性规划、层次分析法以及人工神经网络建模等方法。通过模仿本书讲授的计算过程,读者可以加深对有关数学方法的认识和理解,并且掌握很多Matlab的应用技巧。代码规范,可研究价值高。
-Code based on the Matlab software, tells the story of a large number of
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Fisher 线性判别分析算法matlab实现及感知器算法实现-Fisher linear discrimination analysis,perceptron
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改进型线性判别分析以及新提出的关于两个变量之间距离的比较方法
-Improved linear discriminant analysis and a new method for the comparison of the distance between two variables
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小波分析,根据病人数据进行小波分析并进行判别分析-Wavelet analysis, wavelet analysis and discriminant analysis based on patient data
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判别样本所属类别的方法,主要包括Fisher判别、朴素Bayes判别和距离判别等-The method for determining the sample belongs to the category of specific methods include Fisher discriminant, discriminant and Bayes discriminant distance
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ldatrace performs Linear Discriminant Analysis with optional-Ldatrace performs Linear Discriminant Analysis with optional
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多视图鉴别分析的matlab代码,该代码对应的论文为:2015年PAMI上发表的《Multi-view Discriminant Analysis》(This is the matlab code of the Multi-view Discriminant Analysis (MvDA) algorithm)
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