搜索资源列表
Wavelet+regression+estimation
- Deubechies小波核下的杂波估计效果以及残留噪声的高斯性和独立性检验实验结果演示程序。
Wavelet+regression+estimation
- Deubechies小波核下的杂波估计效果以及残留噪声的高斯性和独立性检验实验结果演示程序。-Wavelet Deubechies lower-estimated the effect of clutter as well as the residual of the Gaussian noise and the independence of test results demo.
dimension
- 代码用于估计关联维数。包括G-P算法(corrint.m)，高斯核关联算法(gka.m) 和Judd算法(judd.m)-Correlation dimension estimation code. Algorithms for estimating the correlation dimension using the grassberger-Proccacia approach (corrint.m), the Gaussian-K
mainKDEprogramLINEAR
- 核密度估计，用于识别和核密度计算，采用高斯插样。-kernel density evalue.it is used for statistical pattern recognition.
gkdj
- 以为高斯和密度估计，使用高斯核的非参数密度估计方法，对样本进行概率密度估计，程序中给出了窗宽的估算公式。-That the Gaussian and density estimation, using Gaussian kernel non-parametric density estimation method, the sample probability density estimates, the program gives t
KDE
- 对6个样本点，进行直方图估计核高斯核密度估计-for 6 sample points, histogram estimation and Gauss kernel density estimation
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 w
rvptirgi
- 对于初学者具有参考意义，采用热核构造权重，利用贝叶斯原理估计混合logit模型的参数，本程序的性能已经达到较高水平，独立成分分析算法降低原始数据噪声，可以广泛的应用于数据预测及数据分析,欢迎大家下载学习。- For beginners with a reference value, Thermonuclear using weighting factors Bayesian parameter estimation principle
KDE
- 核函数估计(一元，高斯核函数)，包括带宽优化-kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable.
MutualInfo
- 通过高斯核密度估计计算多元变量之间的互信息熵-The mutual information entropy between multivariate variables is calculated by Gaussian kernel density estimation
kde
- 给定样本点，采用高斯核密度估计，求出概率密度分布函数。(It is good to use this method to evaluate pdf)
predict
- 基于高斯核函数对数据进行偏最小二乘估计，并进行回归分析(Partial Least Squares Estimation of Data Based on Gauss Kernel Function)