網(wǎng)絡(luò)安全編程之des加密算法實(shí)現(xiàn)有demo
標(biāo)簽： demo des 網(wǎng)絡(luò)安全 編程
上傳時(shí)間： 2014-01-26
上傳用戶(hù)：清風(fēng)冷雨
游程編碼的一個(gè)演示程序, 用VC寫(xiě)的Demo程序, 學(xué)習(xí)Run Length Coding時(shí)很好的參考資料.
標(biāo)簽： Coding Length Demo Run
上傳時(shí)間： 2016-04-11
上傳用戶(hù)：wanghui2438
ICC7AVR v7.13 Pro Loader 1.Install iccv7avr v7.13 demo 2.Copy IccAvrPro713.exe to ICCV7AVR bin folder, 3.Run IccAvrPro713.exe. 4.Enjoy!
標(biāo)簽： 7.13 IccAvrPro ICCV7AVR iccv7avr
上傳時(shí)間： 2013-12-21
上傳用戶(hù)：小碼農(nóng)lz
Keil的HTTP DEMO程序調(diào)試應(yīng)用指南
標(biāo)簽： Keil HTTP DEMO 程序調(diào)試
上傳時(shí)間： 2014-01-27
上傳用戶(hù)：jhksyghr
一個(gè)基于ext的ajax小例子，包括從前臺(tái)到后臺(tái)的完整調(diào)用。 前臺(tái)是jsp加上ext的框架。 后臺(tái)是hibernate-annotations和spring以及dwr的組合。 順便演示了一下用servlet來(lái)返回json數(shù)據(jù)給ext框架的方式。 在grid的演示部分，包括了分頁(yè)的數(shù)據(jù)調(diào)用和如何處理來(lái)自于dwr的數(shù)據(jù)(dwr的部分和官方網(wǎng)站公布的方法一樣) 以及grid的事件處理。 實(shí)例的源代碼中沒(méi)有包括jar包，如果需要運(yùn)行，請(qǐng)根據(jù)jar.jpg所顯示的jar包添加。 數(shù)據(jù)庫(kù)部分請(qǐng)根據(jù)create.sql來(lái)生成。
上傳時(shí)間： 2016-04-14
上傳用戶(hù)：BIBI
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(biāo)簽： Rao-Blackwellised conditional filtering particle
上傳時(shí)間： 2013-12-14
上傳用戶(hù)：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(biāo)簽： Rauch-Tung-Striebel algorithm smoother which
上傳時(shí)間： 2016-04-15
上傳用戶(hù)：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(biāo)簽： sequential reversible algorithm nstrates
上傳時(shí)間： 2014-01-18
上傳用戶(hù)：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(biāo)簽： reversible algorithm the nstrates
上傳時(shí)間： 2014-01-08
上傳用戶(hù)：cuibaigao
C++編寫(xiě)的針對(duì)CP5611 PCI卡的通訊程序Demo
上傳時(shí)間： 2013-12-10
上傳用戶(hù)：kristycreasy
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