C++ GUI Programming with Qt 4
By Jasmin Blanchette, Mark Summerfield
Publisher: Prentice Hall
Pub Date: June 21, 2006
Print ISBN-10: 0-13-187249-4
Print ISBN-13: 978-0-13-187249-3
Pages: 560
thinkinjava2English
Thinking in Java,
2nd Edition, Release 11
To be published by Prentice-Hall mid-June, 2000
Bruce Eckel, President,
MindView, Inc.
Planet PDF brings you the Portable Document
Format (PDF) version of Thinking in Java (2nd
Edition). Planet PDF is the premier PDF-related
site on the web. There is news, software, white
papers, interviews, product reviews, Web links,
code samples, a forum, and regular articles by
many of the most prominent and respected PDF
experts in the world. Visit our sites for more detail:
http://www.planetpdf.com/
http://www.codecuts.com/
http://www.pdfforum.com/
http://www.pdfstore.com/
unix環境高級編程英文版，很經典的一本書
Advanced Programming in the UNIX® Environment: Second Edition
By W. Richard Stevens, Stephen A. Rago
Publisher: Addison Wesley Professional
Pub Date: June 17, 2005
ISBN: 0201433079
Pages: 960
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.
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.
Proceedings of Practice of Interesting Algorithms 2007
The editor assumes no responsibility for the accuracy, completeness or usefulness of
the information disclosed in this volume. Unauthorized use might infringe on
privately owned patents of publication right. Please contact the individual authors for
permission to reprint or otherwise use information from their papers.
First edition 2007
Publication Planned by Prof. Wenxin Li
Edited by Yili Zhao
All rights reserved
by
Artificial Intelligence Laboratory, Peking University
June 26, 2007
function [U,V,num_it]=fcm(U0,X)
％ MATLAB (Version 4.1) Source Code (Routine fcm was written by Richard J.
％ Hathaway on June 21, 1994.) The fuzzification constant
％ m = 2, and the stopping criterion for successive partitions is epsilon =??????.
％*******Modified 9/15/04 to have epsilon = 0.00001 and fix univariate bug********
％ Purpose:The function fcm attempts to find a useful clustering of the
％ objects represented by the object data in X using the initial partition in U0.
The 9th International Conference on Large-Scale Scientific Computations
(LSSC 2013) was held in Sozopol, Bulgaria, during June 3–7, 2013. The conference
was organized and sponsored by the Institute of Information and Communication
Technologies at the Bulgarian Academy of Sciences.
