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模式识别中的算法
ZLJZ概率密度函数的逼近-pattern recognition algorithm ZLJZ probability density function approximation
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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.
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Expectation-Maximization
The EM (Expectation-Maximization) algorithm estimates the parameters of the multivariate probability density function in a form of the Gaussian mixture distribution with a specified number of mixtures.
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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
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Mean Shift 这个概念最早是由Fukunaga等人[1]于1975年在一篇关于概率密度梯度函数的估计中提出来的,其最初含义正如其名,就是偏移的均值向量,在这里Mean Shift是一个名词,它指代的是一个向量,但随着Mean Shift理论的发展,Mean Shift的含义也发生了变化,如果我们说Mean Shift算法,一般是指一个迭代的步骤,即先算出当前点的偏移均值,移动该点到其偏移均值,然后以此为新的起始点,继续移动,直到满足一定的条件结束.
用matlab实现mean shif
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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。
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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. . . .
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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.
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已知一组数据,如何用matlab画出它的概率密度分布图,给出具体程序 并且得出分布图后,如何对图进行积分,进而得到分布函数-Given a set of data, how to draw it with matlab probability density distribution maps, specific procedures are given and draw maps, how points on the graph, and then by distribution funct
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利用正态分布和核密度估计计算分位数。包括正态分布分位数函数、核估计概率密度函数、核估计累计分布概率函数、核估计计算分位数函数。-Normal and kernel density estimation using sub-digit calculation. Including the normal quantile function, kernel estimate probability density function, cumulative distribution probabilit
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高斯混合模型是單一高斯機率密度函數的延伸,由於GMM 能夠平滑地近似任意形狀的密度分佈,因此近年來常被用在語音與語者辨識,得到不錯的效果。-Gaussian mixture model is a single Gaussian probability density function of the extension, as the GMM can approximate arbitrary smooth shape of the density distribution, it is ofte
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模式识别课件
当预先不知道类型数目,或者用参数估计和非参数估计难以确定不同类型的类概率密度函数时,为了确定分类器的性能,可以利用聚类分析的方法。-When the pre-recognition software does not know the type of number, or parameter estimation and non-parameter estimation it is difficult to determine the different types of ca
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自适应核密度估计运动检测方法
提出一种自适应的核密度(kernel density estimation, KDE)估计运动检测算法. 算法首先提出一种自适应前景、背景阈值的双阈值选择方法, 用于像素分类. 该方法用双阈值能克服用单阈值分类存在的不足, 阈值的选择能自适应进行, 且能适应不同的场景. 在此基础上, 本文提出了基于概率的背景更新模型, 按照像素的概率来更新背景, 并利用帧间差分背景模型和KDE分类结果, 来解决背景更新中的死锁问题, 同时检测背景的突然变化. 实验证明了所提出
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Probability Hypothesis Density filter versus Multiple
Hypothesis Tracking
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Probability hypothesis density filter for MTT tracking
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ector quantization is a classical quantization technique from signal processing which allows the modeling of probability density functions by the distribution of prototype vectors. It was originally used for data compression. It works by dividing a l
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probability density function plot
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不同概率分布的概率密度函数、分布的临界值及相应子图作图-The probability density function of different probability distributions, the distribution of the critical value and the corresponding sub Plotting
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Probability distribution implementation in matlab
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计算数据的累计概率密度,采用三次样条插值计算分位点的值,区间预测,里面有具体程序及相关文献。(The cumulative probability density of the calculated data is calculated by three spline interpolation)
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