Provided by: libsvm-tools_3.1-1_i386 bug

NAME

       svm-train  -  train  one or more SVM instance(s) on a given data set to
       produce a model file

SYNOPSIS

       svm-train [-s svm_type ] [ -t kernel_type ] [ -d degree ] [ -g gamma  ]
       [ -r coef0 ] [ -c cost ] [ -n nu ] [ -p epsilon ] [ -m cachesize ] [ -e
       epsilon ] [ -h shrinking ] [ -b probability_estimates ] ] [ -wi  weight
       ] [ -v n ] [ -q ]

       training_set_file [ model_file ]

DESCRIPTION

       svm-train  trains  a Support Vector Machine to learn the data indicated
       in the training_set_file
        and produce a model_file
        to save the results of the learning optimization.  This model  can  be
       used later with svm_predict(1) or other LIBSVM enabled software.

OPTIONS

       -s svm_type
              svm_type  defaults  to 0 and can be any value between 0 and 4 as
              follows:

       0      -- C-SVC

       1      -- nu-SVC

       2      -- one-class SVM

       3      -- epsilon-SVR

       4      -- nu-SVR

       -t kernel_type
              kernel_type defaults to 2 (Radial Basis Function  (RBF)  kernel)
              and can be any value between 0 and 4 as follows:

       0      -- linear: u.v

       1      -- polynomial: (gamma*u.v + coef0)^degree

       2      -- radial basis function: exp(-gamma*|u-v|^2)

       3      -- sigmoid: tanh(gamma*u.v + coef0)

       4      -- precomputed kernel (kernel values in training_set_file) --

       -d degree
              Sets the degree of the kernel function, defaulting to 3

       -g gamma
              Adjusts the gamma in the kernel function (default 1/k)

       -r coef0
              Sets the coef0 (constant offset) in the kernel function (default
              0)

       -c cost
              Sets the parameter C ( cost ) of C-SVC, epsilon-SVR, and  nu-SVR
              (default 1)

       -n nu  Sets  the  parameter  nu  of  nu-SVC,  one-class SVM, and nu-SVR
              (default 0.5)

       -p epsilon
              Set the epsilon in the loss  function  of  epsilon-SVR  (default
              0.1)

       -m cachesize
              Set the cache memory size to cachesize in MB (default 100)

       -e epsilon
              Set  the  tolerance of termination criterion to epsilon (default
              0.001)

       -h shrinking
              Whether to use the shrinking
               heuristics, 0 or 1 (default 1)

       -b probability-estimates
              probability_estimates is a binary value  indicating  whether  to
              calculate  probability  estimates  when  training the SVC or SVR
              model.  Values are 0 or 1 and defaults to 0 for speed.

       -wi weight
              Set the parameter C (cost) of class i  to  weight*C,  for  C-SVC
              (default 1)

       -v n   Set n for n -fold cross validation mode

       -q     quiet mode; suppress messages to stdout.

FILES

       training_set_file  must  be  prepared  in  the  following simple sparse
       training vector format:

       <label> <index1>:<value1> <index2>:<value2> . . .

        .

        .

        .

       There is one sample per line.  Each sample consists of a  target  value
       (label or regression target) followed by a sparse representation of the
       input vector.  All unmentioned coordinates are assumed to  be  0.   For
       classification,  <label>  is  an  integer  indicating  the  class label
       (multi-class is supported). For regression, <label> is the target value
       which  can  be any real number. For one-class SVM, it's not used so can
       be any number.  Except using precomputed kernels (explained in  another
       section),  <index>:<value>  gives a feature (attribute) value.  <index>
       is an integer starting from 1 and <value> is  a  real  number.  Indices
       must be in an ASCENDING order.

ENVIRONMENT

       No environment variables.

DIAGNOSTICS

       None documented; see Vapnik et al.

BUGS

       Please report bugs to the Debian BTS.

AUTHOR

       Chih-Chung  Chang,  Chih-Jen Lin <cjlin@csie.ntu.edu.tw>, Chen-Tse Tsai
       <ctse.tsai@gmail.com> (packaging)

SEE ALSO

       svm-predict(1), svm-scale(1)