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脱机字符识别
- 脱机字符识别:手写数字识别之Fisher线性判别,手写数字识别之模板匹配法,数字识别之神经网络法,细化算法-Offline Character Recognition : handwritten figures identifiable Fisher Linear Discriminant, handwritten figures identifiable template matching method, digital identi
off_line_recognition
- 模式识别中的脱机字符识别，包括手写数字识别之Fisher线性判别，手写数字识别之模板匹配法，数字识别之神经网络法及细化算法。-pattern recognition of Offline Character Recognition, including handwritten digital identification Fisher Linear Discriminant. Handwritten identification tem
worddistinguish
- 脱机字符识别算法，包括手写数字识别之Fisher线性判别，手写数字识别之模板匹配法，数字识别之神经网络法，细化算法 -offline character recognition algorithms, including handwritten digital identification Fisher Linear Discriminant. Handwritten identification template matching
shixieshuzishibiezhifisherxianxingpanbie
- 手写数字识别之Fisher线性判别的程序，是用C++写的，有兴趣的可以参考写-Handwritten numeral recognition of Fisher Linear Discriminant procedure is C++ Written interested can refer to write
LDA
- 线性判别分析（LDA）是一种较为普遍的用于特征提取的线性分类方法。但是将LDA直接用于人耳识别会遇到维数问题和“小样本”问题。人们经过研究，通过多种途径解决了这两个问题并实现了基于LDA的人耳识别。文章对几种基于LDA的人耳识别方法做了理论上的比较和实验数据的支持，这些方法包括Fisherears、DLDA、VDLDA及VDFLDA。实验结果表明VDFLDA是其中最好的一种方法 -Linear Discriminant Analys
characterrecognition
- 脱机字符识别算法，包括手写数字识别之Fisher线性判别，手写数字识别之模板匹配法，数字识别之神经网络法，细化算法 -Offline character recognition algorithms, including the handwritten numeral recognition of Fisher Linear Discriminant, handwritten numeral recognition is templat
fisherInt
- 通过Fisher线性判别是否为数字，并识别手写数字。-Through the Fisher Linear Discriminant for figures and handwritten numeral recognition.
lda
- 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.
Fisher_1
- 该程序应用fisher线性判别对平面两类问题进行分类，应用迭代寻求最佳投影方向。-Application of the program on fisher linear discriminant plane issues two types of classification, the application of iterative searching for the best projection direction.
number_Fisher_linear_recog
- 该源码是手写数字的fisher线性判别。在VC6.0环境下运行，MFC图形化界面，适用于模式识别，人工智能。数字图像处理的参考-The source is a hand-written digit fisher linear discriminant. Run in VC6.0 environment, MFC graphical interface for pattern recognition, artificial intelli
Pattern_recognition2
- 张学工老师模式识别课程二次作业，用fisher线性判别对身高体重二维数据进行性别分类-Zhang engineering curriculum of secondary teachers pattern recognition operations, fisher linear discriminant with two-dimensional data on height and weight, gender-disaggregate
MIMOxitongdexitongrongliang
- 、改写例程，编制用Fisher线性判别方法对三维数据求最优方向W的通用函数。 2、对下面表3-1样本数据中的类别ω1和ω2计算最优方向W。 3、画出最优方向W的直线，并标记出投影后的点在直线上的位置-Rewriting routine, the preparation of Fisher linear discriminant method using three-dimensional data on the directio
PRproject_lda
- 线性判别分析（LDA，全称Fisher Linear Discriminant Analysis）算法的C#实现源码，根据stprtool box for matlab中的LDA.m编写。用到MathNet库做相关的矩阵运算，使用zedgraph控制绘图。有简单的测试数据。- C# source code of linear discriminant analysis (LDA, full name of the Fisher Line
num
- 手写数字识别之Fisher线性判别,手写数字识别之Fisher线性判别-num
Three-kinds-of-template-matching-
- 3种模板匹配法实现的手写数字识别示例,包括手写数字识别之Fisher线性判别、手写数字识别之模板匹配法、细化算法-Three kinds of template matching to achieve the handwritten numeral recognition examples, including handwritten digit recognition of the Fisher linear discriminant
zuoye2
- 同时采用身高和体重数据作为特征，用Fisher线性判别方法求分类器，将该分类器应用到训练和测试样本，考察训练和测试错误情况。将训练样本和求得的决策边界画到图上，同时把以往用Bayes方法求得的分类器也画到图上，比较结果的异同。-At the same time the height and weight data as features, Fisher with linear identifying method for classif
zuoye3
- 用FAMALE.TXT和MALE.TXT的数据作为本次作业使用的样本集，利用K-L变换对该样本集进行变换，与过去用Fisher线性判别方法或其它方法得到的分类面进行比较.-Use FAMALE. TXT and MALE. TXT data as the assignment is to use the data sets, by K-L of the sample set of change, and the past with Fi
1
- 利用Fisher线性判别法求参数权重，并进行类别划分-obtain weight of parameters by Fisher Linear detection, and classify them
T-HOMEWORK
- 用Parzen窗法或者kn近邻法估计概率密度函数，得出贝叶斯分类器，对测试样本进行测试，比较与参数估计基础上得到的分类器和分类性能的差别.2． 同时采用身高和体重数据作为特征，用Fisher线性判别方法求分类器，将该分类器应用到训练和测试样本，考察训练和测试错误情况。将训练样本和求得的决策边界画到图上，同时把以往用Bayes方法求得的分类器也画到图上，比较结果的异同。3．选择上述或以前实验的任意一种方法，用留一法在训练集上估计错误率，与
gender-classification
- Parzen窗法估计概率密度函数，得出贝叶斯分类器 用Fisher线性判别方法求分类器 留一法估计错误率-Parzen Fisher Bayes Leave-one based on height and weight