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The fluid editor is a prototype source code editor supporting a "bring from" exploration model.With the fluid editor programmers can avoid explicit navigation between source documents by incorporating related code into the document under examination
標簽： editor exploration supporting prototype
上傳時間： 2013-12-07
上傳用戶：1079836864
Sector is a system infrastructure software that provides functionality for distributed data storage, access, and analysis/processing. It automatically manages large volumetric data across servers or clusters, even those over distributed wide area high speed networks. Sector provides simple tools and APIs to access and/or process the data. Data and server locations are transparent to users, as the whole Sector network is a single networked super computer to the users.
標簽： infrastructure functionality distributed software
上傳時間： 2013-12-21
上傳用戶：極客
n this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. This detailed discussion of the ABC network should complement the UAI2000 paper by Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. After downloading the file, type "tar -xf demorbpfdbn.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "dbnrbpf" for the demo.
標簽： Rao-Blackwellised conditional filtering particle
上傳時間： 2013-12-17
上傳用戶：zhaiyanzhong
On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
標簽： demonstrates sequential Selection Bayesian
上傳時間： 2016-04-07
上傳用戶：lindor
模式識別學習綜述.該論文的英文參考文獻為303篇.很有可讀價值.Abstract— Classical and recent results in statistical pattern recognition and learning theory are reviewed in a two-class pattern classification setting. This basic model best illustrates intuition and analysis techniques while still containing the essential features and serving as a prototype for many applications. Topics discussed include nearest neighbor, kernel, and histogram methods, Vapnik–Chervonenkis theory, and neural networks. The presentation and the large (thogh nonexhaustive) list of references is geared to provide a useful overview of this field for both specialists and nonspecialists.
標簽： statistical Classical Abstract pattern
上傳時間： 2013-11-25
上傳用戶：www240697738
This book explains how to write, install, and debug device drivers for Windows 2000. It is intended to be a companion to the Microsoft DDK documentation and software. Windows 2000 represents a major improvement to previous versions of Windows NT. Device drivers for Windows 2000 may be designed for the new Windows Driver Model (WDM) architecture. If so, the driver will be source compatible with Windows 98. This book covers the new WDM specification. This book will also prove useful to those studying the internals of Windows 2000, particularly the I/O subsystem and related components.
標簽： explains intended Windows drivers
上傳時間： 2016-04-08
上傳用戶：dreamboy36
Squaring circuits are an important building block for impulse-radio UWB non-coherent receivers. This work proposes a squarer, based on the quadratic law of saturated transistors. Such a circuit has already been proposed for lower frequency applications, therefore this work focuses on the extension to ultra wide bandwidth, with particular care to the consequences related to the deviation from the ideal quadratic law of 0.18μm CMOS transistors.
標簽： impulse-radio non-coherent important receivers
上傳時間： 2013-12-24
上傳用戶：kikye
In this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. This detailed discussion of the ABC network should complement the UAI2000 paper by Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. After downloading the file, type "tar -xf demorbpfdbn.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "dbnrbpf" for the demo.
標簽： Rao-Blackwellised conditional filtering particle
上傳時間： 2013-12-14
上傳用戶：小儒尼尼奧
In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
標簽： Rauch-Tung-Striebel algorithm smoother which
上傳時間： 2016-04-15
上傳用戶：zhenyushaw
This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
標簽： sequential reversible algorithm nstrates
上傳時間： 2014-01-18
上傳用戶：康郎
