New users and old of optimization in MATLAB will find useful tips and tricks in this document, as well as examples one can use as templates for their own problems.
Use this tool by editing the file optimtips.m, then execute blocks of code in cell mode from the editor, or best, publish the file to HTML. Copy and paste also works of course.
Some readers may find this tool valuable if only for the function pleas - a partitioned least squares solver based on lsqnonlin.
This is a work in progress, as I fully expect to add new topics as I think of them or as suggestions are made. Suggestions for topics I ve missed are welcome, as are corrections of my probable numerous errors. The topics currently covered are listed below
DataBurn is an Objective-C example which demonstrates some of the features of DRTracks. The sample illustrates how to create a DRFolder from an existing folder on the source disk and burn it to disc, creating a hybrid ISO9660/Joliet/HFS+ data CD. The sample also uses the DiscRecordingUI framework to present the standard burn setup and progress user interfaces.
A Windows FTP client written without CInternetSession or CFtpConnection classes. Demonstrates manual manipulation of Winsock sockets, FTP principles, and GUI concepts such as List controls with in-place label-editing and column sorting, progress indicators, and reading and writing to the Registry
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 book reflects the efforts of a number of experienced Linux professionals to prepare for
the LPIC-2 beta-exam. It is -- and always will be -- a work in progress.
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.
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.
Wavelet Subband coding for speaker recognition
The fn will calculated subband energes as given in the att tech paper of ruhi sarikaya and others. the fn also calculates the DCT part. using this fn and other algo for pattern classification(VQ,GMM) speaker identification could be achived. the progress in extraction is also indicated by progress bar.
The purpose of Software Project Tracking and Oversight is to provide adequate visibility into actual progress so that management can take effective actions when the project s performance deviates significantly from the plans.
SensorSimII is the framework of a simulator that I have been working on to study how future sensor networks should operate. the simulator is written in a modular fashion so that it can be adapted to serve a number of needs. However, please remember that it is still a work in progress. This web page is here just to give a glimpse of the approach we ve taken with this simulator. Likewise this web page is simply preliminary information to attempt to answer some of the questions that researchers might have about this project.
