搜索资源列表
parzenchuang
- 一个演示parzen窗的演示小程序，参数自己去改阿！
Parzen
- parzen算法的vc实现代码，包括正态分布和均匀分布。-vc achieve parzen algorithm code, including the normal distribution and uniform distribution.
pattern-recognition-simulation
- 用mushrooms数据对模式识别课程讲述的各种模式分类方法[线性分类，Bayesian分类，Parzen窗，KNN]和特征选择和降维方法[PCA,LDA]进行了模拟，并给出了各类分类方法的结果，-It s the simulations about linear classification ,Bayesian ,Parzen and KNN of pattern recognition .And ,It gives the results.
Parzen
- 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
Parzen
- 我搜集的有关parzen窗的估计的相关源码,PPT和相关文献.-I collected estimates of the parzen relevant source window, PPT and related literature.
Parzen
- Parzen窗函数 模式识别 matlab-Parzen window in matlab
Parzen
- p窗法的小程序，采用MFC编程，供大家参考，希望对大家有用。-p applet window method, the use of MFC programming, for your reference, and hope to be useful.
Parzen-window
- 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
parzen
- PARZEN窗法处理正态分布样本的matlab实现程序- Matlab PARZEN window method to achieve program
gmm_utilities
- This collection of MATLAB files perform operations on Gaussian mixture models (GMMs) and Gaussian kernels (ie, Parzen windows). These utilities do evaluation, sampling, multiplication, convolution, linear transformation, mixture reduction, etc. Prese
三步搜索法
- 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 nea
kernel_eca-master
- Kernel Entropy Component Analysis,KECA方法的作者R. Jenssen自己写的MATLAB代码，文章发表在2010年5月的IEEE TPAMI上面-Kernel Entropy Component Analysis, by R. Jenssen, published in IEEE TPAMI 2010.(We introduce kernel entropy component analysis (kernel ECA) as a new method fo
occzarence
- Parzen窗和K近邻法进行概率密度估计还带一个示波器控件()
KECA_Journal_Article
- Robert Jenssen 撰写论文原文(We introduce kernel entropy component analysis (kernel ECA) as a new method for data transformation and dimensionality reduction. Kernel ECA reveals structure relating to the Renyi entropy of the input space data set, estimated