gusucode.com > 支持向量机工具箱 - LIBSVM OSU_SVM LS_SVM源码程序 > 支持向量机工具箱 - LIBSVM OSU_SVM LS_SVM\OSU_SVM3.00\demo\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