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

echo off 
%POLDEMO demonstration for using nonlinear SVM classifier with a  
% polynomial keneral. 
echo on;  
 
clc 
%POLDEMO demonstration for using nonlinear SVM classifier with a  
% polynomial keneral. 
%########################################################################## 
% 
%   This is a demonstration script-file for contructing and  
%     testing a nonlinear SVM-based classifier  
%     (with a polynomial kernel) using OSU SVM CLASSIFIER TOOLBOX.  
%   Note that the form of the polynomial kernel is  
%                (Gamma*+Coefficient)^Degree 
% 
%########################################################################## 
 
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 nonlinear SVM classifier (with polynomial kernel)  
%     using the training data 
%   Note that the form of the polynomial kernel is  
%      (Gamma*+Coefficient)^Degree 
% 
%########################################################################## 
pause % Strike any key to continue  
 
% set the value of Degree if you don't want use its default value,  
% which is 3. 
Degree = 5; 
 
% By using this format, the default values of Gamma, Coefficient, 
% u, Epsilon, CacheSize are used.  
% That is, Gamma=1, Coefficient=1, u=0.5, Epsilon=0.001, and CacheSize=45MB 
[AlphaY, SVs, Bias, Parameters, nSV, nLabel]=u_PolySVC(Samples, Labels, Degree); 
 
% 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 
 
pause % Strike any key to continue  
 
% 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