搜索资源列表
GM_EM
- 经典的em算法即期望最大化算法，可用于高斯混合GMM模型和聚类算法，-Classic em algorithm that expectation maximization algorithm can be used for Gaussian mixture models and GMM clustering algorithm,
CHW3
- Expectation Maximization (EM) Algorithm for Gaussian Mixture Model
EM_GMM
- 基于EM算法实现的高斯混合模型数据分类，可以很优秀的对各种数据进行聚类分析，R语言实现-EM algorithm based on Gaussian mixture model data classification, can be very good for a variety of data clustering analysis, R language
emgm
- matlab code for EM algorithm for Gaussian mixture model
mixGaussEm
- matlab m文件 EM算法混合高斯模型-matlab m file EM algorithm Gaussian mixture model
Hidden-Markov-modelling
- Hidden Markov modelling of contourlet transforms for art authentication Bayesian robust hidden Markov model Hidden Markov Models for Molecular Motors When wavelet meet HMM Hidden Markov Tree model of Contourlet Transform EM for HMM Multivar
fit_mix_gauss
- 对参数使用最大期望算法进行拟合成混合型高斯分布。-fit parameters for a mixed-gaussian distribution using EM algorithm
EMAlgorithm
- 运用EM算法生成任意条件下的二维高斯分布-Using the EM algorithm to generate a two-dimensional Gaussian distribution in any condition
GMMPEM
- 代码给出了高斯分布下的EM算法的设计与实现-Code gives the design and implementation of a Gaussian distribution under the EM algorithm
EM_suanfa
- 基于EM算法的混合高斯分布参数估计方法，包括权值、均值和标准差-Mixed Gaussian distribution parameter estimation method based on the EM algorithm, weights, including the mean and standard deviation
EmGMM
- 高斯混合模型的最大期望迭代求解算法,可用于图像区域灰度分布估计-Expectation maximazation(EM) for Gaussian mixture model(GMM)
GMM-latentSpace-v2.0
- GMM算法，利用EM算法求解混合模型中每个模型的参数-Gaussian Mixture Model,GMMalgorith,Use EM algorith
EM_CD
- 基于高斯混合模型和EM（Expectation Maximization）算法的SAR影像变化监测算法，并附带示例。总体思路是首先将两个时期的SAR影像做log和ratio运算，生成差分影像，然后通过EM算法估计高斯混合模型的参数，最后根据高斯混合模型最大概率，生成变化监测结果。-Unsupervised change detection method for SAR images using EM algorithms of Gaussian mixture model
mixBern
- Just like EM of Gaussian Mixture Model, this is the EM algorithm for fitting Bernoulli Mixture Model. GMM is useful for clustering real value data. However, for binary data (such as bag of word feature) Bernoulli Mixture is more suitable.
ghsc267762131214061
- gaussian-mixture-models: This tool clusters the input image into n number of colored sections by synthesizing a Gaussian Mixture Model (GMM) using Expectation Maximization (EM).
gmm
- Clustering of data points using Gaussian Mixture Model and EM Algorithm
RCY-GMMtest1
- 高斯混合模型（GMM,Gaussian Mixture Model）参数如何确立这个问题，详细讲解期望最大化（EM,Expectation Maximization）算法的实施过程。(How to establish the parameters of Gauss mixture model and explain the implementation process of the expectation maximization algorithm in detail.)
machine_learning_python-master
- 通过阅读网上的资料代码，进行自我加工，努力实现常用的机器学习算法。感知机的基本形式和对偶形式的实现 Kmeans和Kmeans++的实现 EM GMM高斯混合和GMM+LASSO的实现 实现朴素贝叶斯的基本算法和高斯混合朴素贝叶斯算法 实现决策树的基本算法 实现adaboost基本算法 实现svm基本算法 实现逻辑回归基本算法(By reading the data codes on the Internet, we can process oursel