-
GTK SWF Player. GNOME Applications and a mozilla plugin. Other things included is a library for reading swf files, swf2txt convert swf to text files.
標簽：
Applications
included
mozilla
library
上傳時間：
2013-12-23
上傳用戶：TF2015
-
The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB algorithm can be interpreted as an efficient stochastic mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are generic and suitable for any application. For details, please refer to Rao-Blackwellised Particle Filtering for Fault Diagnosis and On Sequential Simulation-Based Methods for Bayesian Filtering After downloading the file, type "tar -xf demo_rbpf_gauss.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab and run the demo.
標簽：
filtering
particle
Blackwellised
conditionall
上傳時間：
2014-12-05
上傳用戶：410805624
-
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
-
The algorithms are coded in a way that makes it trivial to apply them to other problems. Several generic routines for resampling are provided. The derivation and details are presented in: Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas and Eric Wan. The Unscented Particle Filter. Technical report CUED/F-INFENG/TR 380, Cambridge University Department of Engineering, May 2000. After downloading the file, type "tar -xf upf_demos.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "demo_MC" for the demo.
標簽：
algorithms
problems
Several
trivial
上傳時間：
2014-01-20
上傳用戶：royzhangsz
-
％％％ Demos for PUMA algorithms ％％％
We present four matlab demos for PUMA. demo1, demo2, demo3, and demo4
illustrate PUMA working with different parameters and with four
different images.
All you need to do is to run each of the demos. Please be sure that
all the files are put on an accessible path for matlab.
Notice that this code is intended for research purposes only.
For further reference see "Phase Unwrapping via Graph Cuts,
IEEE Transactions on Image Processing, 2007
標簽：
demo
PUMA
algorithms
for
上傳時間：
2016-04-23
上傳用戶：fhzm5658
-
在WinXP中文+tomcat6.0中測試通過。將解壓縮后的整個文件夾放在tomcat/webapps/下面即可，通過http://localhost:8080/lyb訪問。
修正版修改了一處bug：
在將文件放入tomcat/webapp下面后，用瀏覽器打開看時會出錯，原因是tomcat默認裝在 program files 下面，于是得到的路徑含有空格。
經過修改連接函數，現在已經可以正常顯示。
如果又興趣學習jsp的朋友可以下載看看，里面包航所以的源代碼和實現方法。
標簽：
tomcat
webapps
WinXP
6.0
上傳時間：
2016-04-25
上傳用戶：gaojiao1999
-
brew sdk2.0 for vs2005 產生C++ hellowolrd示例程序框架的向導工程
使用方法：
1、解壓縮
2、將BREWAppWizardForCpp文件夾拷到vc2005的向導目的下(比如c:\Program files\Microsoft Visual Studio 8\VC\VCWizards)
3、將BREWAppWizardForCpp.vsdir和BREWAppWizardForCpp.vsz拷到C:\Program files\Microsoft Visual Studio 8\VC\vcprojects下
4、重新啟動VS2005，就可以看到創建項目換向導了。
標簽：
BREWAppWizardForCpp
hellowolrd
brew
2005
上傳時間：
2016-04-27
上傳用戶：15071087253
