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parzen.rar
- 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. . . .
pos-study-
- ：将研究区域划分成许多具有固定长、宽、高且密度均匀的长方体，利用重力的可叠加性，计算了观测点重力异 常，在此基础上形成重力异常的目标函数。把长方体的密度作为参数，采用蚁群算法进行密度反演试验。结果表明该方法 具有一定的科学性和实用性。-: The study area is divided into a number with a fixed length, width, height and density, uniform rectangular, stackable nature
GM_PHDpaper
- IEEE trans. 发表的高斯概率假设密度滤波的开创性文章-It is published in an IEEE tans. on Gaussian mixture probability hypothesis density filter and finite random set
closeForm_PHD
- IEEE trans上发表的高斯混合概率假设密度滤波的证明性论文-It is presened for the GM probability hypothesis density filter and finite random set
EM_Algorithm
- 介绍期望最大算法基本原理及聚类实现，可以很好的对多个高斯概率密度分布进行分类-Introduces the basic principle and expectation maximization clustering algorithm to achieve, can be good for multiple Gaussian probability density distribution of the classification
dbscan
- DBSCAN(Density-Based Spatial Clustering of Applications with Noise)是一个比较有代表性的基于密度的聚类算法。与划分和层次聚类方法不同，它将簇定义为密度相连的点的最大集合，能够把具有足够高密度的区域划分为簇，并可在噪声的空间数据库中发现任意形状的聚类。 -DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a more represent
dbscan
- 数据挖掘算法 dbscan 基于密度的聚类算法 它将簇定义为密度相连的点的最大集合，能够把具有足够高密度的区域划分为簇，并可在噪声的空间数据库中发现任意形状的聚类-Data mining algorithms dbscan density-based clustering algorithm will cluster is defined as the density of points connected to the largest collection of regional divisi
clustering
- 基于快速搜索数据密度峰值的聚类算法是一种基于聚类中心具有较近邻点有更高密度且其与更高密度点间有着较大的相对距离的一类算法。-Clustering by fast search and find of density peaks is based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively large distance
probability-estimation
- 给定若干三维数据，建立训练概率模型，并对新数据进行估计。包括高斯模型、Parzen窗和K近邻密度估计-Given a number of three-dimensional data, the establishment of training probability model, and the new data is estimated. Including the Gaussian model, Parzen windows and K nearest neighbor density e
GMM
- 聚类算法之高斯混合模型，GMM 和 k-means 很像，不过 GMM 是学习出一些概率密度函数来（所以 GMM 除了用在 clustering 上之外，还经常被用于 density estimation ）。-Gaussian mixture model of clustering algorithm, GMM and k-means like, but GMM is learning some probability density function (so GMM except on cl
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- 混合高斯概率密度模型,其参数估计可以通过期望最大化( EM) 迭代算法获得,()