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[AI-NN-PRParzen_KNN

Description: Parzen 窗 和 K近邻法进行概率密度估计 还带一个示波器控件.-Parzen window and K-nearest neighbor method probability density is estimated to bring an oscilloscope control.
Platform: | Size: 51200 | Author: 肖龙远 | Hits:

[Graph Recognizepatternreg

Description: 模式识别中的Parzen窗法ISODATA算法H-K(Ho-Kashyap)算法的实现,用matlab和VC编写-pattern recognition of Parzen window ISODATA method algorithm H-K (Ho-Kashya p) algorithm implementation using Matlab and VC prepared
Platform: | Size: 79872 | Author: | Hits:

[AI-NN-PRParzen

Description: Parzen窗函数概率密度估计演示程序 完全按照《现代模式识别》孙即祥著作 2.4.4《动态聚类法》算法3实现 使用欧式距离作为测度标准。-Parzen window probability density function is estimated demo program in full accordance with the
Platform: | Size: 1024 | Author: 潘水洋 | Hits:

[Windows Developparzenchuang

Description: 一个演示parzen窗的演示小程序,参数自己去改阿!-A demonstration of the demonstration of small Parzen window procedure, the parameters themselves changed Ah!
Platform: | Size: 1024 | Author: wangxiangyu | Hits:

[matlabParzen

Description: Parzen窗法,由样本求概密估计,例为一维正态分布和双峰均匀分布,内附随机数生成的原理说明。-Parzen window method, by the sample density estimate for estimated Example for one-dimensional normal distribution and bimodal uniform distribution, random number generator containing a description of the principle.
Platform: | Size: 134144 | Author: 赵瑞峰 | Hits:

[matlabparzen

Description: 模式识别中parzen窗估计源程序matlab-Pattern Recognition Parzen window estimate matlab source
Platform: | Size: 1024 | Author: xxf | Hits:

[OtherparzenWindows

Description: 应用Parzen窗法估计样本的概率密度函数; 估计样本为标准正态分布和均匀分布;-Application of Parzen window method estimates the probability density function of the sample estimated sample standard normal distribution and uniform distribution
Platform: | Size: 1024 | Author: 张刚 | Hits:

[matlabparzen

Description: Parzen窗估计法的C语言代码和Matlab应用的动态链接库。-Parzen window estimation method of C language code and Matlab applications, dynamic link library.
Platform: | Size: 36864 | Author: g | Hits:

[AI-NN-PRparzen

Description: 这是一个parzen窗口分类算法,希望能给大家的学习有所帮助-This is a Parzen window classification algorithm, we hope that they will be helpful to the learning
Platform: | Size: 2048 | Author: 王霞 | Hits:

[AI-NN-PRParzen_Window

Description: 模式识别中Parzen窗函数的实现,模拟对二维高斯分布的非参估计。窗函数有三种:高斯窗、指数窗、方窗-Pattern Recognition Parzen window function of the realization, simulation of two-dimensional Gaussian distribution is estimated non-participation. There are three window function: Gaussian window, the index window, side windows
Platform: | Size: 4096 | Author: 唐盛 | Hits:

[matlabparzen

Description: parzen窗法,功能是根据样本进行概率密度函数估计。实现了对正态分布概率密度函数和均匀分布双峰概密函数进行估计-Parzen window method, function is based on a sample of the estimated probability density function. The realization of the normal distribution probability density function and uniform distribution Bimodal density function is estimated over
Platform: | Size: 3072 | Author: 陈坚 | Hits:

[GUI DevelopParzen

Description: 调节窗口大小,来捕获数据,而得到函数特征-Adjust window size to capture the data, and function characteristics have been
Platform: | Size: 5184512 | Author: 陈西 | Hits:

[matlabgaussianparzenwindowdensityestimation

Description: parzen window density estimation with Gaussian as a smoothing factor
Platform: | Size: 2048 | Author: killtheemo | Hits:

[matlabparzen

Description: 分类器的训练与学习是模式识别的一个重要环节,其目的在于按照某种算法,确定判决规则,使之具有自动分类识别的能力。本文介绍了采用Parzen窗法的随机模式分类器,并matlab实现了一个简易的随机模式分类器。-Classifier training and learning is an important part of pattern recognition, in accordance with the purpose of some kind of algorithm to determine the decision rules to identify with the ability of automatic classification. This paper introduces the Parzen window method using a random pattern classifier, and achieved a simple matlab random pattern classifier.
Platform: | Size: 287744 | Author: 丑力 | Hits:

[AI-NN-PRparzen

Description: 用parzen窗方法,估计概率密度,采用高期核函数-With parzen window means of estimating the probability density function using high nucleus. . . .
Platform: | Size: 1024 | Author: 刘二多 | Hits:

[matlabclassifier

Description: 两类二维相关正态分布条件下的最小错误率贝叶斯分类器,基于最小风险的贝叶斯分类器,Parzen窗法非参数估计分类器程序,Fisher线性判别法分类器程序。-Under normal conditions two types of two-dimensional correlation of minimum error rate of Bayesian classifier, the minimum risk-based Bayesian classifier, Parzen window method estimates non-parametric classification procedures, Fisher Linear Discriminant classifier program.
Platform: | Size: 39936 | Author: wanwei | Hits:

[matlabparzen

Description: Parzen窗拟合标准正态分布(Matlab实现)-Parzen window fitting the standard normal distribution (Matlab implementation)
Platform: | Size: 288768 | Author: 崔庆君 | Hits:

[matlabparzen

Description: 二维数据集Parzen方窗非参数估计PDF(概率密度函数),三维结果显示,有图,有完整说明文档和程序运行说明,matlab编程环境,此为模式识别小作业 parzen-Dimensional data set Parzen Window non-parametric estimation side PDF (probability density function), three-dimensional results show that map, with complete documentation, matlab programming environment, this is the pattern recognition of small operations
Platform: | Size: 337920 | Author: tianshimao | Hits:

[Special EffectsParzen-window-

Description: Parzen 窗估计密度函数对图形图像进行处理-Parzen window estimated density function of the graphics processing
Platform: | Size: 30720 | Author: Dean | Hits:

[matlabParzen-window-method

Description: 此为模式识别中Parzen窗法估计概率密度函数。 全部程序流程如下: 1、读取FAMALE.TXT文件把身高或体重给数组,并求x1的样本数N1和窗宽、体宽; 2、读取MALE.TXT文件把身高或体重给数组,并求x2的样本数N2和窗宽、体宽; 3、读取Test2.txt文件把对应的身高或体重给数组A并求A的样本数M; 4、利用Parzen窗法估计概率密度函数判别男女性别; 5、对本判别的错误率进行统计。 -This is the pattern recognition Parzen window method to estimate the probability density function . All program flow is as follows : 1 , read FAMALE.TXT file to height or weight to the array, and find the number of samples x1 N1 and window width , body width 2 , read MALE.TXT file to height or weight to the array, and find the number of samples x2 N2 and window width , body width 3 , read Test2.txt file to the corresponding height or weight requirements for the array A and A is the number of samples M 4 , the use of Parzen window method to estimate the probability density function of gender discrimination 5 , determine the error rate of the statistics .
Platform: | Size: 3072 | Author: 崔杉 | Hits:
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