svmocas - train a binary linear SVM classifier

       svmocas    was    written   by   Vojtech   Franc   <xfrancv@cmp.felk.cvut.cz>   and   Soeren   Sonnenburg
       <Soeren.Sonnenburg@tu-berlin.de>.

       This manual page was written by Christian Kastner <ckk@debian.org> for the Debian  project  (and  may  be
       used by others).

                                                  June 16, 2010                                       SVMOCAS(1)

svmocas  is  a  program  that  trains  a  binary  linear SVM classifier using the Optimized Cutting Plane
       Algorithm for Support Vector Machines (OCAS) and produces a model file.

       example_file is a file with training examples in SVM^light format, and model_file is the file in which to
       store the learned linear rule f(x)=w'*x+w0. model_file contains d lines, where d is the  number  of  data
       dimensions. The first n lines are coordinates of w and the last line is w0.

       Train  the  binary  SVM  classifier  from  riply_trn.light,  with  the regularization constant C=10, bias
       switched on, verbosity switched off, and save model to svmocas.model:

                    svmocas -c 10 -b 1 -v 0 riply_trn.light svmocas.model

       Compute the testing error of the classifier stored in  svmocas.model  with  linclassif(1)  using  testing
       examples from riply_tst.light and save the predicted labels to riply_tst.pred:

                    linclassif -e -o riply_tst.pred riply_tst.light svmocas.model

       svmocas - train a binary linear SVM classifier

       A summary of options is included below.

       Generaloptions:-h     Show summary of options.

       -v(0|1)
              Set the verbosity level (default: 1)

       Learningoptions:-cfloat
              Regularization constant C. (default: 1)

       -Cconstants_file
              If  specified,  each  example  has  a  different regularization constant, taken from the text file
              constants_file. Each line of the text file must contain a single constant  (positive  double)  for
              the corresponding example. If -C is used, then the -c option is ignored.

       -b(0|1)
              Value of the L2-bias feature. A value of 0 implies not having bias.  (default: 0)

       -ninteger
              Use  only  the  first  integer  examples  for  training.  By default, integer equals the number of
              examples in example_file.

       Optimizationoptions:-m(0|1)
              Solver to be used:

                   0 ... standard cutting plane (equivalent to BMRM, SVM^perf)

                   1 ... OCAS (default)

       -sinteger
              Cache size for cutting planes. (default: 2000)

       -pinteger
              Number of threads. (default: 1)

       Stoppingconditions:-afloat
              Absolute tolerance TolAbs: halt if QP-QD<=TolAbs. (default: 0)

       -rfloat
              Relative tolerance TolAbs: halt if QP-QD<=abs(QP)*TolRel.  (default: 0.01)

       -qfloat
              Desired objective value QPValue: halt is QP<=QPValue. (default: 0)

       -tfloat
              Halts if the solver time (loading time  is  not  counted)  exceeds  the  time  given  in  seconds.
              (default: infinity)

msvmocas(1), linclassif(1).

svmocas [options] example_filemodel_file