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,
GMMaEM_Classifier
- 采用GMM模型并用EM算法训练分类器，用Train.txt里的数据进行训练，用Test.txt的数据进行性能检测-Using GMM classifier model and EM training algorithm to train the classifier, use the training data in Train.txt to train the classifier and use the data in the Test.txt to test the perform
EM-Algorithm-for-GMM
- Expectation Maximization algorithm for Gaussian Mixture Model Training
GMM_Examples_v2014_08_04
- GMM PARAMETERS AN BE EASILY IMPLIMENTEED USING EM TECHNIQUE.EVENTHOUGH THIS MODEL IS COMPLEX ,E XPECTATION MAXIMAISATION FINDS AN EASYWAY TO CALCULATE THE PARAMETERS IN AN EFFICIENT WAY.
RespirationPatternAnalysis
- GMM PARAMETERS AN BE EASILY IMPLIMENTEED USING EM TECHNIQUE.EVENTHOUGH THIS MODEL IS COMPLEX ,E XPECTATION MAXIMAISATION FINDS AN EASYWAY TO CALCULATE THE PARAMETERS IN AN EFFICIENT WAY.
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)
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).
emgmm
- em algorithm for g-em algorithm for gmm
EM_GMM-master
- em-gmm-master good for gmm algorithm
52957362
- EM算法估计GMM的matlab版本的源代码,适合给类机器学习问题,-EM algorithm to estimate GMM version of the matlab source code, suitable for such machine learning problems,
92920914
- EM algorithm to estimate GMM version of the matlab source code, suitable for such machine learning problems,
ML
- GMM高斯混合模型EM算法聚类，PCA主成分分析，以及从人脸图像中提取主成分(GMM Gauss hybrid model EM algorithm clustering, PCA principal component analysis, and extraction of principal components from face images)
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
5G 中的 SCMA Turbo 学习
- 稀疏码多址 (SCMA) 是最近设计的一种扩展技术，其中 QAM 符号被映射到不同的 OFDMA 音调。基于码本的映射可以看作是一种扩频编码过程，其中整形增益有助于提高频谱效率并增强系统性能。本文基于 SCMA 编码方案，提出了一种联合多用户检测 (MUD) 和信道解码方法，应用了“turbo 原理”。与传统的分离检测和解码方案相比，turbo检测性能更好，增益更高，复杂度适中。在此之上，提出了一种改进方法，即在迭代之前修改外在信息。改进是基于信息的可靠性，保持可靠的信息并减少不可靠的信息。具体