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基于贝叶斯网络的半监督聚类集成模型
- 已有的聚类集算法基本上都是非监督聚类集成算法,这样不能利用已知信息,使得聚类集成的准确性、鲁棒性和稳定性降低.把半监督学习和聚类集成结合起来,设计半监督聚类集成模型来克服这些缺点.主要工作包括:第一,设计了基于贝叶斯网络的半监督聚类集成(semi-supervised cluster ensemble,简称SCE)模型,并对模型用变分法进行了推理求解;第二,在此基础上,给出了EM(expectation maximization)框架下的具体算法;第三,从UCI(University of Ca
opencv em算法
- Expectation-Maximization The EM (Expectation-Maximization) algorithm estimates the parameters of the multivariate probability density function in a form of the Gaussian mixture distribution with a specified number of mixtures.
GMM
- Source code - create Gaussian Mixture Model in following steps: 1, K-means 2, Expectation-Maxximization 3, GMM Notice: All datapoints are generated randomly and you can config in Config.h-Source code- create Gaussian Mixture Model
empca
- I present an expectation-maximization (EM) algorithm for principal component analysis (PCA).
RAM
- 使用ISE的XST综合，综合结果使用了Block RAM，当然有时对于用到的容量很小的RAM，我们并不需要其使用Block RAM，那么只要稍微修改一下就可以综合成Distribute RAM-The use of ISE s XST synthesis, the combined result of the use of the Block RAM, it is our expectation. Of course, sometimes the capacity to use a very s
registration_EM
- It actually simulates the registration process of multiple dissimilar sensors in a wireless sensor network using the expectation maximization algorithm.
iccsa06_1
- Expectation-maximization algorithm
Ch04
- Expectation-maximization algorithm
Ch05
- Expectation-maximization algorithm
ExpectationFromIIS
- This docement is related to Expectation From IIS
modelbasedonspectrumprediction
- 文章展示了基于高斯混合模型的语音频谱预测方法。频谱预测可能在传包过程中预防丢包这方面起到大作用。期望最大化算法用两倍或三倍的连续语音因素来测试模型。模型被用来设计第一，儿等指令预测量。预测表用频谱分配状态来估计并和一个简单的参考模型对比。最好的预测表得到一个平均频率扭曲值是0.46dB小于参考模型-This paper presents methods for speech spectrum prediction based on Gaussian mixture models. Spec
EM_algorithm.pdf
- Good tutorial for Expectation maximization algorithm
A-Bayesian-Approach
- In this paper, we propose a Bayesian methodology for receiver function analysis, a key tool in determining the deep structure of the Earth’s crust.We exploit the assumption of sparsity for receiver functions to develop a Bayesian deconvolution
eScholarship-UC-item-1rb70972
- Expectation maximization and mixture model tutorial
Fergus-Perona
- We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constellations of parts. A probabilistic representation is used for al
EMcanshuguji
- 利用EM算法来实现参数估计，两个等概率事件-expectation max
Km
- In data mining, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. This results in a partitioning of the data space into Vo
Clustering with Gaussian Mixtures
- This documents explains in details with examples of the use Expectation Maximisation algorithm for maximum likelihood estimation in Gaussian mixtures.
Gupta-and-Chen---2010---Theory
- This introduction to the expectation–maximization (EM) algorithm provides an intuitive and mathematically rigorous understanding of EM. Two of the most popular applications of EM are described in detail: estimating Gaussian mixture models (GMMs),
Geometry-of-the-EM-
- 本文为对最大期望算法的一个介绍，从解析几何角度分析了算法的特性和几何意义，对从事机器学习的人有较*价值。-An excellent introduction for Expectation Maximum algorithm. In this paper, a geometric view of the EM algorithm is given, which might be