Description: 本书共分６篇，第１篇统计学基础知识与ＳＡＳ软件应用技巧，介绍了统计学的基本概念和学习方法、试验设计入门、统计描述、ＳＡＳ软件应用入门、编写ＳＡＳ实用程序的技巧、单变量统计分析和利用SAS／GRAPH模块绘制常用统计图的方法。第２篇试验设计与定量资料的统计分析，介绍了与ｔ检验、非参数检验和各种方差分析有关的试验设计和数据处理方法。第３篇试验设计与定性资料的统计分析，介绍了处理二维及高维列联表资料的各种统计分析 方法，包括卡方检验、Fisher的精确检验、典型相关分析、logistic回归模型和对数线性模型等内容。第４篇试验设计与回归分析，介绍了回归分析的种类和选用方法、简单直线回归、多项式回归、简单曲线回归、多元线性回归、协方差分析、直接试验设计及其资料的回归分析等有关内容。第５篇生存分析，介绍了生存资料的特点、生存时间函数和生存分析 方法的分类等基本概念；生存资料的非参数分析方法、ＣＯＸ模型分析方法和参数模型的回归分析方法。第６篇多元统计分析，介绍了主成分分析、因子分析、对应分析、聚类分析、判别分析、典型相关分析。-The book is divided into six, a statistically based knowledge and skills in SAS software applications, introduced the basic concepts in statistics and learning methods, experimental design entry, statistical description of SAS software application entry, to prepare SAS utility skills, single variable statistical analysis and use SAS / GRAPH mapping module commonly used statistical map. The first two experimental design and quantitative analysis of the statistical data, presented with t-test, non - parametric tests and the analysis of variance test design and data processing methods. The first three test design and qualitative information on the statistical analysis of two-dimensional processing and high-dimensional data table shown in the various statistical analysis methods, including chi Platform: |
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Author:苏吉 |
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Description: 本书共分６篇，第１篇统计学基础知识与ＳＡＳ软件应用技巧，介绍了统计学的基本概念和学习方法、试验设计入门、统计描述、ＳＡＳ软件应用入门、编写ＳＡＳ实用程序的技巧、单变量统计分析和利用SAS／GRAPH模块绘制常用统计图的方法。第２篇试验设计与定量资料的统计分析，介绍了与ｔ检验、非参数检验和各种方差分析有关的试验设计和数据处理方法。第３篇试验设计与定性资料的统计分析，介绍了处理二维及高维列联表资料的各种统计分析 方法，包括卡方检验、Fisher的精确检验、典型相关分析、logistic回归模型和对数线性模型等内容。第４篇试验设计与回归分析，介绍了回归分析的种类和选用方法、简单直线回归、多项式回归、简单曲线回归、多元线性回归、协方差分析、直接试验设计及其资料的回归分析等有关内容。第５篇生存分析，介绍了生存资料的特点、生存时间函数和生存分析 方法的分类等基本概念；生存资料的非参数分析方法、ＣＯＸ模型分析方法和参数模型的回归分析方法。第６篇多元统计分析，介绍了主成分分析、因子分析、对应分析、聚类分析、判别分析、典型相关分析。-The book is divided into six, a statistically based knowledge and skills in SAS software applications, introduced the basic concepts in statistics and learning methods, experimental design entry, statistical description of SAS software application entry, to prepare SAS utility skills, single variable statistical analysis and use SAS/GRAPH mapping module commonly used statistical map. The first two experimental design and quantitative analysis of the statistical data, presented with t-test, non- parametric tests and the analysis of variance test design and data processing methods. The first three test design and qualitative information on the statistical analysis of two-dimensional processing and high-dimensional data table shown in the various statistical analysis methods, including chi Platform: |
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Author:苏吉 |
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Description: The function produce a set of features matched through a pair of views.
The selection is driven by the estimate uncertainty extracted from the Fisher information matrix. Platform: |
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Author:张丽娜 |
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Description: bout an unobservable parameter. It can be computed from knowledge of the likelihood function defining the system. For example, with a normal likelihood function, the Fisher information is the reciprocal of the variance of the law. In the absence of knowledge of the likelihood law, the Fisher information may be computed from normally distributed score data as the reciprocal of their seco Platform: |
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Author:testandoestacoisa |
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Description: a good paper introducing Fisher Information matrix-A User Manual for the Fisher Information Matrix Platform: |
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Author:gu bo |
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Description: 详细介绍了Fisher分类器的基本原理以及应用，是模式识别学习不错的资料-Described in detail the basic principles and application of the Fisher classifier is a pattern recognition learning good information Platform: |
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Author:tim |
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Description: 通过最大似然估计的线性调频分解，计算费舍尔信息矩阵，使用优化过程，计算一个似然函数的海赛矩阵等。-Through the maximum likelihood estimation of linear frequency modulation decomposition, calculate fisher information matrix, the use of optimization process, calculate a likelihood function of the hessian matrix, etc.
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Author:小小 |
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Description: FM调制和解调，matlab语言实现，可以改变fs，fc，ft - Fisher4Cast
Authors: Bruce A. Bassett, Yabebal Fantaye, Renee Hlozek and Jacques Kotze
The Fisher4Cast suite provides a standard, tested tool set for general Fisher Information matrix prediction and forecasting for use in both research and education. The toolbox design is robust and modular, allowing for easy additions and adaptation while keeping the user interface intuitive and easy to use. Fisher4Cast is completely general but the default code is written for cosmology. It provides parameter error forecasts for cosmological surveys that provide distance, Hubble expansion and growth measurements in a general, curved FLRW background. See the accompanying paper, http://arxiv.org/abs/0906.0993 , for further details and examples of novel applications in Observational Cosmology.
The release package contains documentation (Manual, Quickstart guide and sample code to produce figures) in addition to the code which can be run in both command line and GUI format (th Platform: |
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Author:刘欢 |
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Description: 基于Fisher information的优化阵列设计，使用穷举法和迭代法得到优化阵列，再基于最大似然估计研究其MSE、克拉美罗界等性能。-Fisher information based on the optimized array design, use brute-force methods and iterative methods optimize the array, and then based on the maximum likelihood estimation study the MSE performance of Cramer-Rao Bound. Platform: |
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Author:冯文 |
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Description: 模式识别的经典算法，它是在1996年由Belhumeur引入模式识别和人工智能领域的。性鉴别分析的基本思想是将高维的模式样本投影到最佳鉴别矢量空间，以达到抽取分类信息和压缩特征空间维数的效果，投影后保证模式样本在新的子空间有最大的类间距离和最小的类内距离，即模式在该空间中有最佳的可分离性。因此，它是一种有效的特征抽取方法。使用这种方法能够使投影后模式样本的类间散布矩阵最大，并且同时类内散布矩阵最小。就是说，它能够保证投影后模式样本在新的空间中有最小的类内距离和最大的类间距离，即模式在该空间中有最佳的可分离性。-Classic pattern recognition algorithm, which is introduced in 1996 by Belhumeur pattern recognition and artificial intelligence. The basic idea of discriminant analysis of high-dimensional pattern is projected onto the sample optimal discriminant vector space, in order to achieve the classification of information extraction and compression feature space dimension effect, the projected sample assurance model in the new sub-space of the largest class the minimum distance and within-class distance, mode has the best separability in the space. Therefore, it is an effective method of feature extraction. Between the use of this method enables projection mode sample class scatter matrix largest and the smallest in the class scatter matrix simultaneously. That is, it can ensure that the samples have a minimum projection mode classes and maximum distance between the distance class, that has the best model separability in the space in the new space. Platform: |
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Author:王永存 |
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Description: The VLFeat open source library implements popular computer vision algorithms specializing in image understanding and local featurexs extraction and matching. Algorithms incldue Fisher Vector, VLAD, SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, SLIC superpixes, quick shift superpixels, large scale SVM training, and many others. It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout. It supports Windows, Mac OS X, and Linux. The latest version of VLFeat is 0.9.19. Platform: |
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Author:zhaiyunlong |
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Description: PCA分类，用于较好的去噪降维，matlab的各种自适应仿真分析。。自适应信息处理的算法、方案繁多，究其实质可归纳为遵循最小均方误差(Least Mean Square，LMS)准则及最小二乘-PCA classification for better denoising dimensionality reduction, a variety of adaptive matlab simulation analysis. . Adaptive information processing algorithms, programs many, their essence can be summarized as follows the minimum mean square error (Least Mean Square, LMS) and the least squares criterion Platform: |
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Author:zhangjun |
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Description: VLAD VLAD可以理解为是BOF和fisher vector的折中
BOF是把特征点做kmeans聚类，然后用离特征点最近的一个聚类中心去代替该特征点，损失较多信息；
Fisher vector是对特征点用GMM建模，GMM实际上也是一种聚类，只不过它是考虑了特征点到每个聚类中心的距离，也就是用所有聚类中心的线性组合去表示该特征点，在GMM建模的过程中也有损失信息；
VLAD像BOF那样，只考虑离特征点最近的聚类中心，VLAD保存了每个特征点到离它最近的聚类中心的距离；
该代码主要应用在视频处理中对于提取特征使用VLAd编码。
-VLAD VLAD can be understood as a compromise BOF BOF and fisher vector is to do kmeans feature point cluster, and then a feature point nearest cluster center to replace the feature point, the loss of more information Fisher vector is the feature point modeling with GMM, GMM actually a cluster, but it is considered a feature point to the distance to each cluster center, which is a linear combination of all the cluster center to represent the feature point in the GMM process modeling is also a loss of information VLAD like BOF, as only consider feature points the nearest cluster center, VLAD stored for each feature point to the nearest cluster center distance the code is mainly used in video processing for use in extracting features VLAd encoding. Platform: |
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Author:周思洁 |
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Description: VLFeat是一个跨平台的开源机器视觉库，它囊括了当前流行的机器视觉算法，如SIFT, MSER, HOG, 同时还包含了诸如K-MEANS, Hierarchical K-means的聚类算法。它由C语言编写，并提供了Matlab接口及详细的文档。当前最新的版本是VLFeat 0.9.18 。(The VLFeat open source library implements popular computer vision algorithms specializing in image understanding and local features extraction and matching. Algorithms include Fisher Vector, VLAD, SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, SLIC superpixels, quick shift superpixels, large scale SVM training, and many others. It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout. It supports Windows, Mac OS X, and Linux. The latest version of VLFeat is 0.9.20.) Platform: |
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Author:mgx0225
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Description: 最小冗余最大相关性（MRMR）（MRMR.M）
需要外部库。详情请见MRMR。下载一个更新版本的互信息工具箱
偏最小二乘（PLS）回归系数（ReGCOEF.m）
使用MATLAB统计工具箱中的PLSReress
ReliefF（分类）和RReliefF（回归）（ReleFracePr.M.）
从Matlab STATS工具箱中包装Releff.m。这是Matlab R2010B以后提供的。
ReliefF的另一个选择是使用ASU特征选择工具箱中的代码。这使用WEKA工具箱的ReleFEF，因此需要额外的库。请参阅相应的文档。
费雪评分（Fisher评分）
围绕ASFS特征选择工具箱围绕FSFisher。M(Minimum Redundancy Maximum Relevance (mRMR) (mRMR.m)
Needs external library. See mRMR.m for details.
Download a newer version of the mutual information toolbox
Partial Least Squares (PLS) regression coefficients (regCoef.m)
Uses plsregress.m from MATLAB statistics toolbox
ReliefF (classification) and RReliefF (regression) (relieffWrapper.m)
Wraps around relieff.m from the MATLAB stats toolbox. This is available MATLAB r2010b onwards.
Another option for ReliefF is to use the code from ASU Feature Selection toolbox. This uses ReliefF from weka toolbox and hence needs additional libraries. Please see the corresponding documentation.
Fisher Score (fisherScore.m)
Wraps around fsFisher.m from the ASU Feature Selection toolbox) Platform: |
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Author:smilingcost |
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Search - fisher information