THIS DESIGN IS PROVIDED TO YOU "AS IS". XILINX MAKES AND YOU RECEIVE NO WARRANTIES OR CONDITIONS, EXPRESS, IMPLIED, STATUTORY OR OTHERWISE, AND XILINX SPECIFICALLY DISCLAIMS ANY IMPLIED WARRANTIES OF MERCHANTABILITY, NON-INFRINGEMENT, OR FITNESS FOR A PARTICULAR PURPOSE. This design has not been verified on hardware (as opposed to simulations), and it should be used only as an example design, not as a fully functional core. XILINX does not warrant the performance, functionality, or operation of this Design will meet your requirements, or that the operation of the Design will be uninterrupted or Error free, or that defects in the Design will be corrected. Furthermore, XILINX does not warrant or make any representations regarding use or the results of the use of the Design in terms of correctness, accuracy, reliability or otherwise.
標簽：
CONDITIONS
WARRANTIES
YOU
PROVIDED
上傳時間：
2016-03-21
上傳用戶：1427796291
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.
標簽：
Rauch-Tung-Striebel
algorithm
smoother
which
上傳時間：
2016-04-15
上傳用戶：zhenyushaw
