Ho Chi Minh City University of Technology
Computer Science department
Distributed Computing Environment (DCE)
Written by Nguyen Van Noi -59406114
nvnoi76@yahoo.com
Hidden Markov Toolkit (HTK) 3.2.1
HTK is a toolkit for use in research into automatic speech recognition and has been developed by the Speech Vision Robotics Group at the Cambridge University Engineering department (http://svr-www.eng.cam.ac.uk) and Entropic Ltd (http://www.entropic.com).
Open Source for the Enterprise
Open source software is changing the world of Information Technology. But making it work for your company is far more complicated than simply installing a copy of Linux. If you are serious about using open source to cut costs, accelerate development, and reduce vendor lock-in, you must institutionalize skills and create new ways of working. You must understand how open source is different from commercial software and what responsibilities and risks it brings. Open Source for the Enterprise is a sober guide to putting open source to work in the modern IT department.
This data set contains WWW-pages collected from computer science departments of various universities in January 1997 by the World Wide Knowledge Base (Web->Kb) project of the CMU text learning group. The 8,282 pages were manually classified into the following categories:
student (1641)
faculty (1124)
staff (137)
department (182)
course (930)
project (504)
other (3764)
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.
算法導論英文版
This book is one of a series of texts written by faculty of the Electrical Engineering and
Computer Science department at the Massachusetts Institute of Technology. I
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.
