www.pudn.com > osu_svm3.00.zip > c_lindemo.m

echo off 
%LINDEMO demonstration for using linear SVM classifier. 
echo on;  
 
clc 
%LINDEMO demonstration for using linear SVM classifier. 
%########################################################################## 
% 
%   This is a demonstration script-file for contructing and testing a linear 
%   SVM-based classifier using OSU SVM CLASSIFIER TOOLBOX.  
% 
%########################################################################## 
 
pause % Strike any key to continue (Note: use Ctrl-C to abort) 
 
clc 
%########################################################################## 
% 
%   Load the training data and examine the dimensionity of the data 
% 
%########################################################################## 
pause % Strike any key to continue  
 
% load the training data 
clear all 
load DemoData_train 
 
pause % Strike any key to continue  
 
% take a look at the data, and please pay attention to the dimensions  
% of the input data  
who 
 
size(Labels)  
size(Samples) 
 
pause % Strike any key to continue  
 
clc 
%########################################################################## 
% 
%   Construct a linear SVM classifier using the training data 
% 
%########################################################################## 
pause % Strike any key to continue  
 
% Constructing using the most simple format. 
% By using this format, the default values of C, Epsilon, CacheSize 
% are used. That is, C=1, Epsilon=0.001, and CacheSize=35MB 
[AlphaY, SVs, Bias, Parameters, nSV, nLabel] = LinearSVC(Samples, Labels); 
 
 
% End of the SVM classifier construction  
% 
% The resultant SVM classifier is jointly determined by  
%  "AlphaY", "SVs", "Bias", "Parameters", and "Ns". 
% 
 
pause % Strike any key to continue  
 
% Save the constructed linear SVM classifier  
save SVMClassifier AlphaY SVs Bias Parameters nSV nLabel; 
 
pause % Strike any key to continue  
 
 
clc 
%########################################################################## 
% 
%   Test the constructed linear SVM Classifier 
% 
%########################################################################## 
pause % Strike any key to continue  
 
% Load the constructed linear SVM classifier 
clear all 
load SVMClassifier 
 
pause % Strike any key to continue  
 
% have a look at the variables determining the SVM classifier 
who 
 
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% load test data 
load DemoData_test 
 
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% Test the constructed SVM classifier using the test data 
% begin testing ... 
[ClassRate, DecisionValue, Ns, ConfMatrix, PreLabels]= SVMTest(Samples, Labels, AlphaY, SVs, Bias,Parameters, nSV, nLabel); 
% end of the testing 
 
pause % Strike any key to continue  
 
% The resultant confusion matrix of this 4-class classification problem is: 
ConfMatrix 
 
pause % Strike any key to continue  
 
 
echo off