Pseudo Driver Test Demo I/O control
標簽： control Pseudo Driver Demo
上傳時間： 2016-04-06
上傳用戶：思琦琦
Pseudo Driver Test Demo ShareFiles Basic
標簽： ShareFiles Pseudo Driver Basic
上傳時間： 2014-12-02
上傳用戶：wlcaption
測試stc89C58單片機 測試stc89C58單片機 測試stc89C58單片機DEMO 程序
上傳時間： 2014-01-06
上傳用戶：hj_18
BOOSTING DEMO, A VERY USEFUL DEMO FOR ADABOOST
標簽： DEMO BOOSTING ADABOOST USEFUL
上傳時間： 2013-12-08
上傳用戶：chenxichenyue
AT91SAM7S64 demo source code
上傳時間： 2013-12-14
上傳用戶：tonyshao
SUNPLUS(凌陽）GPC1XX demo程序，6502指令
上傳時間： 2013-12-23
上傳用戶：lmeeworm
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
介紹水晶報表使用的一個文檔,其中有在WINDOWS中使用水晶報表的DEMO
上傳時間： 2013-11-29
上傳用戶：363186
LIBSVM是臺灣大學林智仁(Lin Chih-Jen)副教授等開發設計的一個簡單、易于使用和快速有效的SVM模式識別與回歸的軟件包，他不但提供了編譯好的可在Windows系列系統的執行文件，還提供了源代碼，方便改進、修改以及在其它操作系統上應用；該軟件還有一個特點，就是對SVM所涉及的參數調節相對比較少，提供了很多的默認參數，利用這些默認參數就可以解決很多問題；并且提供了交互檢驗(Cross Validation)的功能。
上傳時間： 2014-01-08
上傳用戶：417313137
