网络安全编程之des加密算法实现有demo
上传时间： 2014-01-26
上传用户：清风冷雨
游程编码的一个演示程序, 用VC写的Demo程序, 学习Run Length Coding时很好的参考资料.
上传时间： 2016-04-11
上传用户：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!
标签： 7.13 IccAvrPro ICCV7AVR iccv7avr
上传时间： 2013-12-21
上传用户：小码农lz
Keil的HTTP DEMO程序调试应用指南
上传时间： 2014-01-27
上传用户：jhksyghr
一个基于ext的ajax小例子，包括从前台到后台的完整调用。 前台是jsp加上ext的框架。 后台是hibernate-annotations和spring以及dwr的组合。 顺便演示了一下用servlet来返回json数据给ext框架的方式。 在grid的演示部分，包括了分页的数据调用和如何处理来自于dwr的数据(dwr的部分和官方网站公布的方法一样) 以及grid的事件处理。 实例的源代码中没有包括jar包，如果需要运行，请根据jar.jpg所显示的jar包添加。 数据库部分请根据create.sql来生成。
上传时间： 2016-04-14
上传用户：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.
标签： Rao-Blackwellised conditional filtering particle
上传时间： 2013-12-14
上传用户：小儒尼尼奥
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.
标签： Rauch-Tung-Striebel algorithm smoother which
上传时间： 2016-04-15
上传用户：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.
标签： sequential reversible algorithm nstrates
上传时间： 2014-01-18
上传用户：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.
标签： reversible algorithm the nstrates
上传时间： 2014-01-08
上传用户：cuibaigao
C++编写的针对CP5611 PCI卡的通讯程序Demo
上传时间： 2013-12-10
上传用户：kristycreasy
