% 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 the Minimal Primal Dual SVM is used with SVM
% regularization parameter C=1.2 and a Gaussian kernel of width 2.1 and 10MB of
% kernel cache and the precision parameter epsilon=1e-5.
% 
% For more details on the MPD solver see
%  Kienzle, W. and B. Schölkopf: Training Support Vector Machines with Multiple
%  Equality Constraints. Machine Learning: ECML 2005, 182-193. (Eds.) Carbonell,
%  J. G., J. Siekmann, Springer, Berlin, Germany (11 2005)

% MPDSVM
print MPDSVM

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 MPDSVM
svm_epsilon 1e-5
svm_use_bias 0
c 0.017

train_classifier

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