使用說明(使用mat檔).txt

libsvm(matlb SVM code)可以使用來作為分類

1.load heart_scale.mat


2.璸衡―秆
model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 0.07');

Usage: model = svmtrain(training_label_vector, training_instance_matrix, 'libsvm_options');
'libsvm_options'	
-s svm_type : set type of SVM (default 0)
0 -- C-SVC	
1 -- nu-SVC	
2 -- one-class SVM
3 -- epsilon-SVR	
4 -- nu-SVR
-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*|u-v|^2)
3 -- sigmoid: tanh(gamma*u'*v + coef0)
4 -- precomputed kernel (kernel values in training_instance_matrix)
-d degree : set degree in kernel function (default 3)
-g gamma : set gamma in kernel function (default 1/k)
-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 weight*C, for C-SVC (default 1)
-v n: n-fold cross validation mode\n"

 
3.璸衡w 蛤 b
w = model.SVs' * model.sv_coef;
b = -model.rho;
if model.Label(1) == -1
w = -w;
b = -b;
end

4.璸衡training error
[predict_label, accuracy, dec_values] = svmpredict(heart_scale_label, heart_scale_inst, model);