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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 relative importance (amplitude) of each component.
標簽： algorithm Expectation-Maximization iterative optimi
上傳時間： 2015-06-17
上傳用戶：獨孤求源
一個在matlab環境下編寫的采用expectation maximization方法計算高斯混合模型的程序。
標簽： maximization expectation matlab 環境
上傳時間： 2014-01-16
上傳用戶：dongbaobao
用matlab語言寫的EM(Expectation maximization)算法,用于模式分類
標簽： maximization Expectation matlab EM
上傳時間： 2014-01-09
上傳用戶：ls530720646
I present an Expectation-Maximization (EM) algorithm for principal component analysis (PCA).
標簽： Expectation-Maximization algorithm component principal
上傳時間： 2014-01-10
上傳用戶：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.
標簽： instantaneous algorithm Bayesian Gaussian
上傳時間： 2013-12-19
上傳用戶：jjj0202
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 communications and has been the driver for the gen- esis and deployment of three generations of cellular systems in the space of 20 years.
標簽： Deploying Wireless Networks
上傳時間： 2020-05-27
上傳用戶：shancjb
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.
標簽： Maximization Nonconvex Wireless Utility Systems in
上傳時間： 2020-06-01
上傳用戶：shancjb
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 transmission systems is becoming increasingly rigorous due to continually evolving functions of interconnected power networks from operation jurisdiction to control responsibly – coupled with the rising demand and expectation for reliability.
標簽： Monitoring Protection Wide Area
上傳時間： 2020-06-07
上傳用戶：shancjb
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 students that my other books in this area have. Unlike the traditional books available in the area of power electronics, this book has a unique presentation format that makes it convenient for group presentations that use Microsoft’s PowerPoint software. In fact, a disk is included that has a PowerPoint file on it that is ready for presentation with the core figures. Presentations can also be organized using just selected portions of the book
標簽： Electronics Advances Drives Power Motor And
上傳時間： 2020-06-10
上傳用戶：shancjb
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 grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propa- gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications.
標簽： Bishop-Pattern-Recognition-and-Ma chine-Learning
上傳時間： 2020-06-10
上傳用戶：shancjb
