Here we are at the crossroads once again
Youre telling me youre so confused
You cant make up your mind
Is this meant to be
Youre asking me
Trademark
But only love can say - try again or walk away
But I believe for you and me
The sun will shine one day
So Ill just play my part
And pray you ll have a change of heart
But I cant make you see it through
Thats something only love can do
Face to face and a thousand miles apart
Ive tried my best to make you see
Theres hope beyond the pain
If we give enough if we learn to trust
[Chorus]
I know if I could find the words
To touch you deep inside
Youd give our dream just one more chance
Dont let this be our good-bye
This function obtains a unitary matrix Q such that: d=diag(Q *diag(lmd)*Q).
In other words, it gives a way to generate a matrix with given eigenvalues and diagonal elements.
By Daniel Perez Palomar (last revision: May 10, 2004).
Feel free to distribute this file as it is (without including any modifications).
Writing this book was hard work, but also a lot of fun. Thanks to everyone who made it
possible, especially Jinny Verdonck, Thomas Kraft, Ricky Nkrumah, Kirk Bateman, and the
whole Apress crew. A big thanks goes also to the community that continuously extends and
improves J2ME Polish!
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.
These listed libraries are written in WTL. But it s really hard to mix both MFC & WTL together. Obviously, it s not reasonable to ask a developer or a team to giving up MFC and move to the WTL world just because there were some great controls or visual Frameworks written in WTL (there are many things that should be considered especially in a company with hundreds of developers like the company I work for). Unfortunately, there was no such good and free visual Framework in MFC until now. Whatever difficulties there are, I still desire to be able to use them and now my effort is here to be shared with you.
Under the Hood
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.
The algorithms are coded in a way that makes it trivial to apply them to other problems. Several generic routines for resampling are provided. The derivation and details are presented in: Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas and Eric Wan. The Unscented Particle Filter. Technical report CUED/F-INFENG/TR 380, Cambridge University Department of Engineering, May 2000. After downloading the file, type "tar -xf upf_demos.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "demo_MC" for the demo.
