m
 
(One intermediate revision by one other user not shown)
Line 1: Line 1:
 
 
You can find an example for classifying a data using Matlab in the following link
 
You can find an example for classifying a data using Matlab in the following link
+
 
  http://www.igi.tugraz.at/lehre/EW/tutorials/nnt_intro/index.html
+
http://www.igi.tugraz.at/lehre/EW/tutorials/nnt_intro/index.html
  
  
 
In the file 'nnt_intro_classification.m' it creates NN with 5 hidden units, 3 output units, logsig activation function for each layer. You need to change the configuration to improve the performance and refer to the pdf file in the downloaded zipped file.
 
In the file 'nnt_intro_classification.m' it creates NN with 5 hidden units, 3 output units, logsig activation function for each layer. You need to change the configuration to improve the performance and refer to the pdf file in the downloaded zipped file.
  
[[Category:Matlab Neural Network Toolbox for Classification]]
+
 
  
  
 
You can download SVM library in the following link
 
You can download SVM library in the following link
  http://www.csie.ntu.edu.tw/~cjlin/libsvm/
+
http://www.csie.ntu.edu.tw/~cjlin/libsvm/
  
 
You need to download the following file from the link
 
You need to download the following file from the link
    MATLAB A simple MATLAB interface LIBSVM authors at National Taiwan University.  2.85  Zip
+
MATLAB A simple MATLAB interface LIBSVM authors at National Taiwan University.  2.85  Zip
  
 
When you run 'make_win.m' in the Matlab command prompt it will generate dll files which are callable in Matlab.
 
When you run 'make_win.m' in the Matlab command prompt it will generate dll files which are callable in Matlab.
 
Before running 'make_win.m' you need to setup the default compiler used by mex by running 'mex -setup'
 
Before running 'make_win.m' you need to setup the default compiler used by mex by running 'mex -setup'
  
 +
Datasets at UCI Irwin Machine Learning laboratory ..
 +
http://archive.ics.uci.edu/ml/datasets.html
 +
 +
[[Category:Matlab Neural Network Toolbox for Classification]]
 
[[Category:Downloadable SVM libraries in C and how to run in Matlab]]
 
[[Category:Downloadable SVM libraries in C and how to run in Matlab]]
 +
 +
== One Comparison Between SVM and Neural Network - Paper ==
 +
 +
 +
There is one paper which compare the performance of SVM and Neural Network for Drug/Non-Drug classificaiton. The below is the link to the paper.
 +
 +
http://pubs.acs.org/cgi-bin/article.cgi/jcisd8/2003/43/i06/pdf/ci0341161.pdf
 +
 +
In the paper,the author applies two method as binary decision problems to classify drug and non-drug materials. The result shows that SVM performs better than Neural Network since SVM has higher accuracy in predicting for new data set.

Latest revision as of 19:30, 30 March 2008

You can find an example for classifying a data using Matlab in the following link

http://www.igi.tugraz.at/lehre/EW/tutorials/nnt_intro/index.html


In the file 'nnt_intro_classification.m' it creates NN with 5 hidden units, 3 output units, logsig activation function for each layer. You need to change the configuration to improve the performance and refer to the pdf file in the downloaded zipped file.



You can download SVM library in the following link http://www.csie.ntu.edu.tw/~cjlin/libsvm/

You need to download the following file from the link MATLAB A simple MATLAB interface LIBSVM authors at National Taiwan University. 2.85 Zip

When you run 'make_win.m' in the Matlab command prompt it will generate dll files which are callable in Matlab. Before running 'make_win.m' you need to setup the default compiler used by mex by running 'mex -setup'

Datasets at UCI Irwin Machine Learning laboratory .. http://archive.ics.uci.edu/ml/datasets.html

One Comparison Between SVM and Neural Network - Paper

There is one paper which compare the performance of SVM and Neural Network for Drug/Non-Drug classificaiton. The below is the link to the paper.

http://pubs.acs.org/cgi-bin/article.cgi/jcisd8/2003/43/i06/pdf/ci0341161.pdf

In the paper,the author applies two method as binary decision problems to classify drug and non-drug materials. The result shows that SVM performs better than Neural Network since SVM has higher accuracy in predicting for new data set.

Alumni Liaison

Ph.D. on Applied Mathematics in Aug 2007. Involved on applications of image super-resolution to electron microscopy

Francisco Blanco-Silva