% In this example a one-class support vector machine classifier is trained on a
% toy data set. The training algorithm finds a hyperplane in the RKHS which
% separates the training data from the origin. The one-class classifier is
% typically used to estimate the support of a high-dimesnional distribution.
% For more details see e.g.
%   B. Schoelkopf et al. Estimating the support of a high-dimensional
%   distribution. Neural Computation, 13, 2001, 1443-1471.
% 
% In the example, the one-class SVM is trained by the LIBSVM solver with the
% regularization parameter C=1.2 and the Gaussian kernel of width 2.1 and the
% precision parameter epsilon=1e-5 and 10MB of the kernel cache.
% 
% For more details on LIBSVM solver see http://www.csie.ntu.edu.tw/~cjlin/libsvm/ .
% 
% 

% LibSVM OneClass
print LibSVMOneClass

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 LIBSVM_ONECLASS
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
