% In this example a two-class linear 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 OCAS solver is used with the SVM
% regularization parameter C=1.2 and the bias term in the classification rule
% switched off. The solver iterates until the relative duality gap falls below
% epsilon=1e-5 or the maximal training time (max_train_time=60 seconds) is
% exceeded.
% 
% For more details on the OCAS solver see
%  V. Franc, S. Sonnenburg. Optimized Cutting Plane Algorithm for Large-Scale Risk
%  Minimization.The Journal of Machine Learning Research, vol. 10,
%  pp. 2157--2192. October 2009.
% 

% SVMOcas
print SVMOcas

set_features TRAIN ../data/fm_train_sparsereal.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier SVMOCAS
svm_epsilon 1e-5
svm_use_bias 0
c 0.42

train_classifier

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