求特征值与特征向量的程序，通过methoDe De LU思想设计。
上传时间： 2014-12-07
上传用户：plsee
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.
标签： presentation Artificial Describes tutorial
上传时间： 2014-01-24
上传用户：qilin
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.
标签： Rao-Blackwellised conditional filtering particle
上传时间： 2013-12-17
上传用户：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.
标签： Demonstrates sequential Selection Bayesian
上传时间： 2016-04-07
上传用户：lindor
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
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.
标签： algorithms problems Several trivial
上传时间： 2014-01-20
上传用户：royzhangsz
ITU-T G.723.1 Speech CoDer: Matlab implementation This package implements the speech coDer and DecoDer. Full documentation is in the PDF file incluDed with the package. The test folDer has test programs for the coDer and DecoDer (for Windows).
标签： implementation implements package Speech
上传时间： 2013-12-05
上传用户：qq521
