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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 re ...

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。

用matlab语言写的EM(Expectation maximization)算法,用于模式分类

I present an expectation-maximization (EM) algorithm for principal component analysis (PCA).

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 ...

At the time of writing, and to an extent never seen before, there is an expectation that almost any information or service that is available through communication systems in the office or home will be available wherever the user happens to be. This is placing incredible demands on wireless communica ...

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, fi ...

Today, electric power transmission systems should face many demanding chal- lenges, which include balancing between reliability, economics, environmental, and other social objectives to optimize the grid assets and satisfy the growing electrical demand. Moreover, the operational environment of trans ...

I am presenting this novel book on advances and trends in power electronics and motor drives to the professional community with the expectation that it will be given the same wide and enthusiastic acceptance by practicing engineers, R&D professionals, univer- sity professors, and even graduate stude ...

Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have g ...