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
模式识别学习综述.该论文的英文参考文献为303篇.很有可读价值.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.
标签： statistical Classical Abstract pattern
上传时间： 2013-11-25
上传用户：www240697738
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
Expert PID Controller，Fuzzy Tunning PID Control，ingle NEURAL Adaptive PID Controller
标签： Controller Expert PID
上传时间： 2016-04-17
上传用户：gaojiao1999
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
aiNet application is a very powerful and a very simple tool for solving the problems which are usually solved with artificial NEURAL networks (ANN). All possible tests we had run proved that the results obtained with aiNet are at least as good as the results obtained with some other ANNs. Let us state some of aiNet抯 features. (c) aiNet 1995-1997
标签： very application powerful problems
上传时间： 2014-01-16
上传用户：wang5829
s file contains the Joone Distributed training Environment (DTE). See http://www.jooneworld.com/docs/dte.html to learn more about it. To learn more about Joone - Java Object Oriented NEURAL Engine: http://www.joone.org Joone and the DTE are both released with the LGPL license @2004 Paolo Marrone and the Joone team - All rights reserved ==================================================================== Credits The Joone DTE uses the following external packages: - SUN Jini Network Technology http://wwws.sun.com/software/jini/index.html - Computefarm Framework http://computefarm.jini.org - Spring Framework http://www.springframework.org We want to thank all the authors and contributors of the above packages. Please read the respective licenses contained in this distribution.
标签： Distributed Environment jooneworld contains
上传时间： 2013-12-25
上传用户：钓鳌牧马
本人编写的incremental 随机神经元网络算法，该算法最大的特点是可以保证approximation特性，而且速度快效果不错，可以作为学术上的比较和分析。目前只适合benchmark的regression问题。 具体效果可参考 G.-B. Huang, L. Chen and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes”, IEEE Transactions on NEURAL Networks, vol. 17, no. 4, pp. 879-892, 2006.
标签： incremental 编写 神经元网络 算法
上传时间： 2016-09-18
上传用户：litianchu
Tricks of the Windows Game Programmin Gurus, 2E takes the reader through Win32 programming, covering all the major components of DirectX including DirectDraw, DirectSound, DirectInput (including Force Feedback), and DirectMusic. Andre teaches the reader 2D graphics and rasterization techniques. Finally, Andre provides the most intense coverage of game algorithms, multithreaded programming, artificial intelligence (including fuzzy logic, NEURAL nets, and genetic algorithms), and physics modeling you have ever seen in a game book.
标签： programming Programmin the covering
上传时间： 2014-01-02
上传用户：wangchong
C++实现神经网路的bp算法，初学者可以参考下 BP algorithm of NEURAL network s achievement based on c++, maybe it is useful to a new learner
上传时间： 2016-11-06
上传用户：aa17807091
