Provided by: libsvm-tools_3.1-1build1_amd64 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)