Keil的HTTP Demo程序調試應用指南
上傳時間： 2014-01-27
上傳用戶：jhksyghr
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
This Demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjDemo1". 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： reversible algorithm the nstrates
上傳時間： 2014-01-08
上傳用戶：cuibaigao
C++編寫的針對CP5611 PCI卡的通訊程序Demo
上傳時間： 2013-12-10
上傳用戶：kristycreasy
ST7540 Demo 下位機C程序，配合ST上微機軟件，有需要得可以參考參考
上傳時間： 2014-01-19
上傳用戶：royzhangsz
agg圖形庫在WINCE下的一個Demo，演示了AGG如何在WINCE下用GDI畫圖。
上傳時間： 2013-12-21
上傳用戶：mikesering
usb isp Demo source code
上傳時間： 2016-04-22
上傳用戶：LIKE
ucfs文件系統pc版的Demo演示，提供ucfs在pc上移植的全套代碼。
上傳時間： 2016-04-23
上傳用戶：chenlong
