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
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
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
Expert PID Controller，F(xiàn)uzzy Tunning PID Control，ingle Neural Adaptive PID Controller
標(biāo)簽： Controller Expert PID
上傳時(shí)間： 2016-04-17
上傳用戶：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.
標(biāo)簽： sequential reversible algorithm nstrates
上傳時(shí)間： 2014-01-18
上傳用戶：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
標(biāo)簽： very application powerful problems
上傳時(shí)間： 2014-01-16
上傳用戶：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.
標(biāo)簽： Distributed Environment jooneworld contains
上傳時(shí)間： 2013-12-25
上傳用戶：釣鰲牧馬
本人編寫的incremental 隨機(jī)神經(jīng)元網(wǎng)絡(luò)算法，該算法最大的特點(diǎn)是可以保證approximation特性，而且速度快效果不錯(cuò)，可以作為學(xué)術(shù)上的比較和分析。目前只適合benchmark的regression問題。 具體效果可參考 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.
標(biāo)簽： incremental 編寫 神經(jīng)元網(wǎng)絡(luò) 算法
上傳時(shí)間： 2016-09-18
上傳用戶：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.
標(biāo)簽： programming Programmin the covering
上傳時(shí)間： 2014-01-02
上傳用戶：wangchong
C++實(shí)現(xiàn)神經(jīng)網(wǎng)路的bp算法，初學(xué)者可以參考下 BP algorithm of Neural network s achievement based on c++, maybe it is useful to a new learner
上傳時(shí)間： 2016-11-06
上傳用戶：aa17807091
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