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Welcome to UnderC version 1.2.9w
This package consists of the executable (UCW), a default script file,
this file, and the library files. It is important that the header files
end up in a include subdirectory of the directory where UCW is found.
If you unzip this file using its path information ( use folder names ) this will
automatically happen. You can optionally specify the UnderC directory
with the environment variable UC_HOME note that this points to the directory
containing ucw.exe. If you do this, then you can copy the executable anywhere
and it will still be able to find the header files.
標(biāo)簽：
executable
consists
Welcome
package
上傳時(shí)間：
2015-12-18
上傳用戶：baiom
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This directory contains the Genetic Algorithm Optimization Toolbox for
Matlab
To use this, if you are local to NCSU and have AFS access to this
directory, simply extend the matlab path using the following command.
You can also place this command in a file called startup.m. Everytime
you start Matlab in the directory containing this file, the path will
always be extended.
標(biāo)簽：
Optimization
Algorithm
directory
contains
上傳時(shí)間：
2014-01-18
上傳用戶：songnanhua
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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(biāo)簽：
Rao-Blackwellised
conditional
filtering
particle
上傳時(shí)間：
2013-12-17
上傳用戶：zhaiyanzhong
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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(biāo)簽：
demonstrates
sequential
Selection
Bayesian
上傳時(shí)間：
2016-04-07
上傳用戶：lindor
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模式識別學(xué)習(xí)綜述.該論文的英文參考文獻(xiàn)為303篇.很有可讀價(jià)值.Abstract— Classical and recent results in statistical pattern
recognition and learning theory are reviewed in a two-class
pattern classification setting. This basic model best illustrates
intuition and analysis techniques while still containing the essential
features and serving as a prototype for many applications.
Topics discussed include nearest neighbor, kernel, and histogram
methods, Vapnik–Chervonenkis theory, and neural networks. The
presentation and the large (thogh nonexhaustive) list of references
is geared to provide a useful overview of this field for both
specialists and nonspecialists.
標(biāo)簽：
statistical
Classical
Abstract
pattern
上傳時(shí)間：
2013-11-25
上傳用戶：www240697738
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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.
標(biāo)簽：
filtering
particle
Blackwellised
conditionall
上傳時(shí)間：
2014-12-05
上傳用戶：410805624
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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：小儒尼尼奧
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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：zhenyushaw
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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：康郎
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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：cuibaigao
