www.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\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 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