% In this example a two-class support vector machine classifier is trained on a
% toy data set and the trained classifier is used to predict labels of test
% examples. As training algorithm Gradient Projection Decomposition Technique
% (GPDT) is used with SVM regularization parameter C=1.2 and a Gaussian
% kernel of width 2.1 and 10MB of kernel cache.
% 
% For more details on GPDT solver see http://dm.unife.it/gpdt
% 
% 

% GPBTSVM
print GPBTSVM

set_kernel GAUSSIAN REAL 10 1.2
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier GPBTSVM
svm_epsilon 1e-5
svm_use_bias 0
c 0.017

train_classifier

set_features TEST ../data/fm_test_real.dat
out.txt = classify
! rm out.txt
