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parzen.根据样本进行概率密度函数估计
- 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 distributi
Parzen-window-method
- 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.rar
- 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
parzen.rar
- 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. . . .
parzen
- 这是一个模式识别中的parzen窗的一个简单仿真分类实例，其中female.txt和male.txt是训练样本，test.txt是测试样本，分类效果非常好，对于模式学习的初学者将会有很大帮助。-This is a pattern recognition in a simple window parzen Category simulation examples, one of female.txt and male.txt training samples, test.txt is the me
Parzen
- 利用parzen窗进行概率密度函数估计，并给出仿真，程序简单易懂。-Using parzen Window probability density function estimation and the simulation, the program is simple to understand.
moshishibie
- 关于模式识别的几个matlab程序，包括fisher；parzen窗；感知函数等等。-Matlab on the number of pattern recognition procedures, including the fisher parzen window perceptual function and so on.
parzen
- 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 documentat
parzen
- 这是parzen窗估计的典型例子，大家可以直接学习，是个不可多得的好材料。-This is a typical example of the estimated parzen window, we can learn, is a rare good material.
parzen
- Parzen窗拟合标准正态分布（Matlab实现）-Parzen window fitting the standard normal distribution (Matlab implementation)
Parzen_Window
- 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, th
parzen
- 这是一个有关parzen窗估计的代码，用来估计概率密度函数，在模式识别中有很多重要的地位-This is a window of the estimated parzen code, used to estimate the probability density function, in the pattern recognition there are many important position ~ ~
gaussianparzenwindowdensityestimation
- parzen window density estimation with Gaussian as a smoothing factor
parzen
- 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
classifier
- 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 meth
proj04-01
- 本实验目的是了解非参数估计—Parzen窗估计，对表格3中的数据进行Parzen窗估计和设计分类器.Parzen窗方法：假设区间Rn是一个d维的超立方体，hn表示超立方体的边长，体积为Vn=hnd。-Purpose of this study is to understand the non-parametric estimation-Parzen window estimate of the data in Table 3 Parzen window estimate and design c
parzen
- 这是模式识别的一个大作业，其中包括了parzen窗的原理，算法和源程序，还有对结果的分析。-This is a great job of pattern recognition, including parzen window theory, algorithms and source code, as well as the results of the analysis.
Pattern-Recognization
- 本实验的目的是了解非参数估计--parzen窗估计，对数据进行Parzen窗估计和设计分类器。并分析窗口宽度h对分类的影响。-The purpose of this study is to understand the non-parametric estimation- parzen window estimation, Parzen window estimate of the data and design of classifiers. Window width h and analyz
parzenmoshishibie
- 用parzen来计算所选的数据的概率密度函数，所选的窗函数是方窗，最后基于最小错误率的贝叶斯进行分类-With parzen selected data to calculate the probability density function, the selected window function is the side window, and finally the smallest error rate based on Bayesian classification
parzen_estimate
- parzen 窗函数 对样本进行有效分类-parzen—window function