Using the libSVM toolbox, there are two main functions, svmtrain and svmpredict, to train and test the data, which can realize multi-classification.
"Usage:
model = svmtrain(train_label,traindata,'libsvm_options');
"libsvm_options:
"-s svm_type: set type of SVM (default 0)"
"0 – C-SVC (multi-class classification)"
" 1 – nu -SVC (multi-class classification)"
"2 – one-class SVM"
" 3 – epsilon-SVR (regression)"
"4 – nu-SVR (regression)"
"-t kernel_type: set type of kernel function (default 2 ) "
" 0 - Linear: U ' V "
". 1 - Polynomial: (Gamma U' V + coef0) Degree ^ "
" 2 - Radial Basis function: exp (-gamma | UV | ^ 2) "
". 3 - Sigmoid:
" 4 – precomputed kernel (kernel values in traindata)"
“-d degree : set degree in kernel function (default 3)”
“-g gamma : set gamma in kernel function (default 1/num_features)”
“-r coef0 : set coef0 in kernel function (default 0)”
“-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)”
“-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)”
“-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)”
“-m cachesize : set cache memory size in MB (default 100)”
“-e epsilon : set tolerance of termination criterion (default 0.001)”
“-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)”
“-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)”
"-wi weight : set the parameter C of class i to weightC, for C-SVC (default 1)"
“-v n: n-fold cross validation mode”
“-q : quiet mode (no outputs)”
"Usage:
[predicted_label, accuracy, decision_values/prob_estimates] = svmpredict(test_label, test_data, model, ‘libsvm_options’)
" model: SVM model structure from svmtrain."
" libsvm_options:"
" -b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); one-class SVM not supported yet"
" -q : quiet mode (no outputs)"
“Returns:”
" predicted_label: SVM prediction output vector."
" accuracy: a vector with accuracy, mean squared error, squared correlation coefficient."
" prob_estimates: If selected, probability estimate vector."