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       shogun -  A Large Scale Machine Learning Toolbox


       shogun [options]


       This manual page briefly documents the readline interface of shogun

       Shogun  is  a  large scale machine learning toolbox with focus on large
       scale kernel methods and especially on Support  Vector  Machines  (SVM)
       with  focus  to  bioinformatics.  It  provides  a  generic  SVM  object
       interfacing to several different SVM implementations. Each of the  SVMs
       can  be combined with a variety of the many kernels implemented. It can
       deal with weighted linear combination of a number of sub-kernels,  each
       of  which not necessarily working on the same domain, where  an optimal
       sub-kernel weighting can be learned  using  Multiple  Kernel  Learning.
       Apart from SVM 2-class classification and regression problems, a number
       of linear methods  like  Linear  Discriminant  Analysis  (LDA),  Linear
       Programming  Machine (LPM), (Kernel) Perceptrons and also algorithms to
       train hidden markov models are implemented. The  input  feature-objects
       can  be  dense, sparse or strings and of type int/short/double/char and
       can be converted into different feature types. Chains of  preprocessors
       (e.g.   substracting  the  mean) can be attached to each feature object
       allowing for on-the-fly pre-processing.


       A summary of options is included below.

       -h, --help, /?
              Show summary of options.

       -i     listen on tcp port 7367 (hex of sg)

              execute a script by reading commands from file <filename>

       when no options are given the interactive readline  interface  will  be

       SEE ALSO
              svm-train(1), svm-predict(1).  svm-scale(1).

              shogun      was      written      by      Soeren      Sonnenburg
              <>   and   Gunnar   Raetsch

       This manual page was written by Soeren  Sonnenburg  <>,
       for the Debian project (but may be used by others).

                                August  1, 2007