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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.
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These are the codes in \"A note on two-dimensional linear discrimant analysis\", Pattern Recognition Letter
In this paper, we show that the discriminant power of two-dimensional discriminant analysis is not stronger than that of LDA under the assu
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关于线性(FISHER)判别分析的中文文献,从核心期刊中下载得到。,About Linear (FISHER) Discriminant Analysis of English literature, from the core journals have been downloaded.
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采用GdI+编程实现了,图像的读入,图像像素数据的采集和分析,通过线性判别函数和一个圆形判别函数来分离图像和背景-By GdI+ programming, and read the image, the image pixel data collection and analysis, linear discriminant function and a circular discriminant function to separate the image and the background
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基于线性判别的人脸识别,本设计对主成分分析和线性判别两种子空间方法进行了深入研究,包括LDA及其在人脸识别应用的若干问题-Face recognition based on linear discriminant, the design of the principal component analysis and linear discriminant subspace methods are two in-depth study, including the LDA and its Appl
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ldatrace performs Linear Discriminant Analysis with optional(Ldatrace performs Linear Discriminant Analysis with optional)
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性判别式分析(Linear Discriminant Analysis, LDA),也叫做 Fisher 线性判别
(FisherLinear Discriminant,FLD), 是模式识别的经典算法(Linear Discriminant Analysis)
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本文件提供了核线性判别分析(KLDA)的将维算法(This program is the kernel linear discriminant analysis)
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线性判别式分析,可以用来对数据进行分类或数据降维(不同于PCA)(Linear discriminant analysis)
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LDA算法 将带上标签的数据(点),通过投影的方法,投影到维度更低的空间中,使得投影后的点,会形成按类别区分,一簇一簇的情况,相同类别的点,将会在投影后的空间中更接近。(Linear Discriminant Analysis)
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2D线性判别分析工具箱,该方法为Pattern recognition letters关于2DLDA的源码(2D linear discriminant analysis toolbox, which is the source code for Pattern recognition letters on 2DLDA)
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LDA的一篇介绍。 LDA的全称是Linear Discriminant Analysis(线性判别分析),是一种supervised learning。LDA是在目前机器学习、数据挖掘领域经典且热门的一个算法(An introduction to LDA. LDA's full name is Linear Discriminant Analysis (Linear Discriminant Analysis), is a supervised learning. LDA is a clas
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本报告对 朴素贝叶斯模型及线性判别分析、二次判别分析 进行系统测试
“生成模型”是机器学习中监督学习方法的一类。与“判别模型”学习决
策函数和条件概率不同,生成模型主要学习的是联合概率分布??(??,??)。本
文中,我们从朴素贝叶斯算法入手,分析比较了几种常见的生成模型(包
括线性判别分析和二次判别分析)应用于多因子选股的异同,希望对本领
域的投资者产生有实用意义的参考价值。(This report gives a systematic test of naive Bayesian
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在MATLAB2014b版本,进行人脸识别,包含PCA降维、LDA线性判别、KNN最近邻算法等技术(In MATLAB2014b version, face recognition is carried out, including PCA dimension reduction, LDA linear discriminant analysis, KNN nearest neighbor algorithm and so on.)
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主成分分析法和线性判别分析常用来对原始数据进行简单的数学分析(Principal component analysis and linear discriminant analysis are usually used for simple mathematical analysis of raw data.)
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airpls扣掉原始数据荧光背景,pls提取主成分,lda进行线性判别分析。程序和数据完整,不懂欢迎询问(Airpls's original data of fluorescence background, pls principal component extraction, linear discriminant analysis. Program and data integrity, welcome to inquire)
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这是关于图像处理的MVDN算法,多视图线性判别分析(This is the image processing MVDN algorithm, multi-view linear discriminant analysis)
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LLDA,局部线性判别分析,可以降到任意的维度,相比LDA更加优秀(LLDA, Local Linear Discriminant Analysis, can be reduced to any dimension, which is better than LDA.)
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用于线性及非线性的判别分析等,简单实用(Simple and practical for linear and non-linear discriminant analysis)
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LDA是一种监督学习的降维技术,也就是说它的数据集的每个样本是有类别输出的,这点和PCA不同。PCA是不考虑样本类别输出的无监督降维技术。
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