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

echo off 
% RBFDEMO demonstration for using nonlinear SVM classifier  
% with a RBF kernel. 
echo on;  
 
clc 
% RBFDEMO demonstration for using nonlinear SVM classifier  
% with a RBF kernel. 
%########################################################################## 
% 
%   This is a demonstration script-file for contructing and  
%     testing a nonlinear  SVM-based classifier  
%     (with a RBF kernel) using OSU SVM CLASSIFIER TOOLBOX.  
%   Note that the form of the RBF kernel is  
%               exp(-Gamma*|X(:,i)-X(:,j)|^2) 
% 
%########################################################################## 
 
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 
Samples=Samples(:,find(Labels==1)); 
Labels = ones(1,size(Samples,2)); 
 
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 nonlinear SVM classifier (with RBF kernel)  
%     using the training data 
%   Note that the form of the RBF kernel is  
%     exp(-Gamma*|X(:,i)-X(:,j)|^2) 
% 
%########################################################################## 
pause % Strike any key to continue  
 
% set the value of Gamma and u if you don't want use its default value,  
Gamma = 2; 
u=0.3; 
% By using this format, the default values of Epsilon, CacheSize 
% are used. That is, Epsilon=0.001, and CacheSize=45MB 
 
[AlphaY, SVs, Bias, Parameters, nSV, nLabel] =one_RbfSVC(Samples, Gamma,u); 
 
 
% 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 nonlinear SVM classifier  
save SVMClassifier AlphaY SVs Bias Parameters nSV nLabel; 
 
pause % Strike any key to continue  
 
 
clc 
%########################################################################## 
% 
%   Test the constructed nonlinear SVM Classifier 
% 
%########################################################################## 
pause % Strike any key to continue  
 
% Load the constructed nonlinear SVM classifier 
clear all 
load SVMClassifier 
 
pause % Strike any key to continue  
 
% have a look at the variables determining the SVM classifier 
who 
 
pause % Strike any key to continue  
 
% load test data 
load DemoData_test 
Samples=Samples(:,find(Labels==1)); 
Labels = ones(1,size(Samples,2)); 
 
pause % Strike any key to continue  
 
% Test the constructed SVM classifier using the test data 
% begin testing ... 
[nonOutlierRate, scores]= SVMTest(Samples, Labels, AlphaY, SVs, Bias,Parameters, nSV, nLabel); 
% end of the testing 
 
pause % Strike any key to continue  
 
% Percentage of Outliers in the whole class 
% Theoretical Value: 
u=0.3 
 
% Experimental Result: 
1-nonOutlierRate 
 
pause % Strike any key to continue  
 
 
echo off