The EM algorithm is short for Expectation-Maximization algorithm. It is based on an iterative optimization of the centers and widths of the kernels. The aim is to optimize the likelihood that the given data points are generated by a mixture of Gaussians. The numbers next to the Gaussians give the relative importance (amplitude) of each component.
标签： algorithm Expectation-Maximization iterative optimi
上传时间： 2015-06-17
上传用户：独孤求源
一个在matlab环境下编写的采用expectation Maximization方法计算高斯混合模型的程序。
标签： Maximization expectation matlab 环境
上传时间： 2014-01-16
上传用户：dongbaobao
用matlab语言写的EM(Expectation Maximization)算法,用于模式分类
标签： Maximization Expectation matlab EM
上传时间： 2014-01-09
上传用户：ls530720646
I present an expectation-Maximization (EM) algorithm for principal component analysis (PCA).
标签： expectation-Maximization algorithm component principal
上传时间： 2014-01-10
上传用户：LIKE
This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by Maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
标签： instantaneous algorithm Bayesian Gaussian
上传时间： 2013-12-19
上传用户：jjj0202
Once upon a time, cellular wireless networks provided two basic services: voice telephony and low-rate text messaging. Users in the network were separated by orthogonal multiple access schemes, and cells by generous frequency reuse patterns [1]. Since then, the proliferation of wireless services, fierce competition, andthe emergenceof new service classes such as wireless data and multimediahave resulted in an ever increasing pressure on network operators to use resources in a moreefficient manner.In the contextof wireless networks,two of the most common resources are power and spectrum—and, due to regulations, these resources are typically scarce. Hence, in contrast to wired networks, overprovisioning is not feasible in wireless networks.
标签： Maximization Nonconvex Wireless Utility Systems in
上传时间： 2020-06-01
上传用户：shancjb
