Provided by: libocas-tools_0.97+dfsg-5_amd64 bug

NAME

       linclassif - predict labels by a linear classification rule

SYNOPSIS

       linclassif [options] example_file model_file

DESCRIPTION

       linclassif is a program that predicts labels by a linear classification rule.

       example_file  is  a  file with testing examples in SVM^light format, and model_file is the
       file which contains either a binary (two-class) rule f(x)=w'*x+w0 or  a  multi-class  rule
       f(x)=W'*x. These are produced svmocas(1) and msvmocas(1), respectively.

OPTIONS

       A summary of options is included below.

       -h     Show summary of options.

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

       -e     Print  the classification error computed from predicted labels and labels contained
              in example_file.

       -o out_file
              Save predictions to the file out_file.

       -t (0|1)
              Output type:

                   0 ... predicted labels (default)

                   1 ... discriminant values

EXAMPLES

       Train the multi-class SVM classifier from example file fiply_trn.light,  using  svmocas(1)
       with  the  regularization  constant  C=10,  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 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

SEE ALSO

       svmocas(1), msvmocas(1).

AUTHORS

       linclassif  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 <debian@kvr.at>, for the Debian  project
       (and may be used by others).

                                         August 24, 2014                            LINCLASSIF(1)