Description: 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-Kernel algorithm (gka.m) and Judd s estimator (judd.m) are provided. Platform: |
Size: 18432 |
Author:彭跃华 |
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Description: 用matlab编写的函数式文件：function r=ksrlin(x,y,h,N).用于进行局部线性拟合，x、y为自、因变量，h为核估计的带宽，N为数据点的个数。-Using matlab functional file: function r = ksrlin (x, y, h, N). Used for local linear fitting, x, y as independent, dependent variable, h is the kernel estimator of bandwidth, N is the data the number of points. Platform: |
Size: 2048 |
Author:于忠达 |
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Description: machine learning-Density Estimation objects.
parzen - Parzen s windows kernel density estimator
indep - Density estimator which assumes feature independence
bayes - Classifer based on density estimation for each class
gauss - Normal distribution density estimator-machine learning-Density Estimation objects.
parzen - Parzen s windows kernel density estimator
indep - Density estimator which assumes feature independence
bayes - Classifer based on density estimation for each class
gauss - Normal distribution density estimator Platform: |
Size: 14336 |
Author:hossein |
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Description: 递归核最小二乘算法，来至MIT大学的wingate教授，含6个源码，有实例！-dict_init.m- Part of the dictionary implementation used by KRLS algorithm. Can stand alone.
dict.m- Part of the dictionary implementation used by the KRLS algorithm. Can stand alone.
krls_init.m- Kernel recursive least squares initializer.
krls.m- Main KRLS function. Repeatedly called with new data points.
krls_query.m- Query the resulting estimator.
krls_example.m- An example script showing the different parts. Platform: |
Size: 4096 |
Author:jiang |
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Description: 横轴和纵轴，可以将样本点表示出来。由于一般第一、二和三主成分包含的样本的信息较多，
经常选其作为横轴和纵轴。当两个样本差异不大时，他们对应的主成分的得分值差异也不会大，
所以可以凭此对样本进行分类。-The quality of the sample
depends on the accuracy of the density estimator chosen, and it needs to decide the kinds of kernel function and the value of its bandwidth and $\alpha$. Besides
there are some drawback about the relation between the value $\alpha$ and the ratio of cluster and outlier. In the future, the extension of
its techniques to data sets of very high dimensionality is possible by taking into account several properties of high-dimensional spaces. Platform: |
Size: 616448 |
Author:宋云胜 |
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Description: 二维高斯核函数重构
重构方法不依赖于参数化模型-2D Gaussian Kernel Reconstruction
fast and accurate state-of-the-art
bivariate kernel density estimator
with diagonal bandwidth matrix.
The kernel is assumed to be Gaussian.
The two bandwidth parameters are
chosen optimally without ever
using/assuming a parametric model for the data or any rules of thumb .
Unlike many other procedures, this one
is immune to accuracy failures in the estimation of
multimodal densities with widely separated modes Platform: |
Size: 4096 |
Author:zty |
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Search - kernel estimator