kpca

This file implement Lda algorithm for dimension reduction and clustering techniques. Dimension reduction, Clustering.

optimizing code & lDPC

应用遗传算法对多目标问题的优化，matlab实现(Gne. algorithm)

基本粒子群算法源程序，开发环境是MATLAB，吐血推荐，易改实用！(Particle swarm algorithm source)

shortest path dijkstra algorithm

matlab小程序，用于kml文件格式转shp格式文件。(it is a matlab progam which can covert kml file to shape file.)

njust随即信号处理课件，最新最全面版本，需要研究随即信号的同学的必备！(njust Random signal processing )

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. (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. )

matlab2011 64位 win7系统 PIDNN test(matlab2011 64bit win7)

this is the jpeg code i try to simulate in matlab