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Description: 和Unix的compress/uncompress兼容的压缩/解压算法16位程序，适合压缩文本或重复字节较多的文件
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Size: 66303 |
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Description: sctp protocol stack
please use winZip to uncompress it and run it on Linux platform-sctp protocol stack please use winZip to un compress it and run it on Linux platform
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Size: 742843 |
Author: arfole |
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Description: This sample shows the necessary code to recompress a WMV file.
It shows reading uncompressed samples, writing uncompressed samples, multi-pass
encoding, multi-channel output, and smart recompression.
-This sample shows the necessary code to rec ompress a WMV file. It shows reading uncompress ed samples, writing uncompressed samples, multi-pass encoding, multi-channel output, and smart recompression.
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Size: 13912 |
Author: jameslee |
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Description: This manual describes how to run the Matlab® Artificial Immune Systems tutorial presentation developed by Leandro de Castro and Fernando Von Zuben. The program files can be downloaded from the following FTP address: ftp://ftp.dca.fee.unicamp.br/pub/docs/vonzuben/lnunes/demo.zip
The tour is self-guided and can be performed in any order.
To run the presentation, first uncompress the zipped archive and store it in an appropriate directory. Run the Matlab® , enter the selected directory, and type “tutorial” in the prompt.
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Size: 92500 |
Author: zhoeng |
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Description: 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.
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Size: 14016 |
Author: 徐剑 |
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Description: 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.
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Size: 16422 |
Author: 徐剑 |
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Description: 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.
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Size: 203207 |
Author: 晨间 |
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Description: 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.
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Size: 128829 |
Author: 晨间 |
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Description: 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.
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Size: 198220 |
Author: 晨间 |
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Description: 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.
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Size: 220044 |
Author: 晨间 |
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Description: 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.
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Size: 348783 |
Author: 晨间 |
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Description: 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.
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Size: 58970 |
Author: 晨间 |
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Description: zLib Compress/uncompress example
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Size: 10925 |
Author: ff3g3f3 |
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Description: Uncompress GZIP file Java example code
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Size: 2012 |
Author: richman |
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Description: Uncompress ZIP file Java example code
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Size: 1832 |
Author: richman |
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Description: Uncompress GZIP file Java example code
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Size: 2048 |
Author: richman |
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Description: 压缩解压源代码，
包含压缩和解压的几个源代码-Extracting compressed source code, including compression and decompression of several source code
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Size: 111616 |
Author: 王波 |
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Description: 檔案壓縮及解壓縮的演算法 記憶體壓縮及解壓縮的演算法-file compress and uncompress memory compress and uncompress
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Size: 46080 |
Author: 王志偉 |
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Description: 对文件进行加压解压处理，对文件进行加压解压处理-Decompression of files to deal with pressure
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Size: 171008 |
Author: song |
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Description: Compress and uncompress file
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Size: 1024 |
Author: zwenkai |
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