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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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Description: 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 measurement, classification effect is very good for beginners will be learning model has very big help. Platform: |
Size: 2048 |
Author:刘锐 |
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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 |
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Description: 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. Platform: |
Size: 3072 |
Author:qewrq |
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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 |
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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 |
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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 |
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Description: 这是一个有关parzen窗估计的代码，用来估计概率密度函数。采用了方窗、指数窗、高斯窗函数三种核函数，附有matlab程序。-This is an estimate of the code related to parzen window, used to estimate the probability density function. With a side window, the index window, Gaussian window function three kinds of kernel function, with matlab program. Platform: |
Size: 56320 |
Author:章 |
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Description: 本实验的目的是学习Parzen窗估计和k最近邻估计方法。在之前的模式识别研究中，我们假设概率密度函数的参数形式已知，即判别函数J(.)的参数是已知的。本节使用非参数化的方法来处理任意形式的概率分布而不必事先考虑概率密度的参数形式。在模式识别中有躲在令人感兴趣的非参数化方法，Parzen窗估计和k最近邻估计就是两种经典的估计法。(The purpose of this experiment is to study the Parzen window estimation and the k nearest neighbor estimation method. In previous pattern recognition studies, we assume that the parameter form of the probability density function is known, that is, the parameter of the discriminant function J (...) is known. This section uses nonparametric methods to handle any form of probability distribution without having to consider the parameter form of probability density. In pattern recognition, there are hidden nonparametric methods that are interesting. Parzen window estimation and K nearest neighbor estimation are two classical estimation methods.) Platform: |
Size: 2048 |
Author:bss
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