NAME

       pkoptsvm - program to optimize parameters for SVM classification

SYNOPSIS

       pkoptsvm -t training [options] [advanced options]

DESCRIPTION

       pkoptsvm  The  support  vector  machine  depends  on  several  parameters.  Ideally, these
       parameters should be optimized for each classification problem.  In case of a radial basis
       kernel  function,  two  important parameters are {cost} and {gamma}.  The utility pkoptsvm
       can optimize these two parameters, based on an accuracy assessment (the Kappa value).   If
       an  input  test set (-i) is provided, it is used for the accuracy assessment.  If not, the
       accuracy assessment is based on a cross validation (-cv) of the training sample.

       The optimization routine uses a  grid  search.   The  initial  and  final  values  of  the
       parameters  can  be set with -cc startvalue -cc endvalue and -g startvalue -g endvalue for
       cost and gamma respectively.  The search uses a  multiplicative  step  for  iterating  the
       parameters (set with the options -stepcc and -stepg).  An often used approach is to define
       a relatively large multiplicative step first (e.g 10) to obtain an  initial  estimate  for
       both  parameters.  The estimate can then be optimized by defining a smaller step (>1) with
       constrained start and end values for the parameters cost and gamma.

OPTIONS

       -t filename, --training filename
              training vector file.  A single vector file contains all training features (must be
              set as: b0, b1, b2,...) for all classes (class numbers identified by label option).

       -i filename, --input filename
              input test vector file

       -cc startvalue -cc endvalue, --ccost startvalue --ccost endvalue
              min and max boundaries the parameter C of C-SVC, epsilon-SVR, and nu-SVR (optional:
              initial value)

       -g startvalue -g endvalue, --gamma startvalue --gamma endvalue
              min max boundaries for gamma in kernel function (optional: initial value)

       -step stepsize, --step stepsize
              multiplicative step for ccost and gamma in GRID search

       -v level, --verbose level
              use 1 to output intermediate results for plotting

       Advanced options

       -tln layer, --tln layer
              training layer name(s)

       -label attribute, --label attribute
              identifier for class label in training vector file.  (default: label)

       -bal size, --balance size
              balance the input data to this number of samples for each class (default: 0)

       -random, --random
              in case of balance, randomize input data

       -min number, --min number
              if number of training pixels is less then min, do not take this class into account

       -b band, --band band
              band index (starting from 0, either use band option or use start to end)

       -sband band, --startband band
              start band sequence number

       -eband band, --endband band
              end band sequence number

       -offset value, --offset value
              offset     value     for     each      spectral      band      input      features:
              refl[band]=(DN[band]-offset[band])/scale[band]

       -scale value, --scale value
              scale      value      for      each      spectral      band     input     features:
              refl=(DN[band]-offset[band])/scale[band] (use 0 if scale min and max in  each  band
              to -1.0 and 1.0)

       -svmt type, --svmtype type
              type of SVM (C_SVC, nu_SVC,one_class, epsilon_SVR, nu_SVR)

       -kt type, --kerneltype type
              type of kernel function (linear,polynomial,radial,sigmoid)

       -kd value, --kd value
              degree in kernel function

       -c0 value, --coef0 value
              coef0 in kernel function

       -nu value, --nu value
              the parameter nu of nu-SVC, one-class SVM, and nu-SVR

       -eloss value, --eloss value
              the epsilon in loss function of epsilon-SVR

       -cache number, --cache number
              cache memory size in MB (default: 100)

       -etol value, --etol value
              the tolerance of termination criterion (default: 0.001)

       -shrink, --shrink
              whether to use the shrinking heuristics

       -cv value, --cv value
              n-fold cross validation mode (default: 0)

       -cf, --cf
              use Overall Accuracy instead of kappa

       -maxit number, --maxit number
              maximum number of iterations

       -tol value, --tolerance value
              relative tolerance for stopping criterion (default: 0.0001)

       -a value, --algorithm value
              GRID,       or       any      optimization      algorithm      from      http://ab-
              initio.mit.edu/wiki/index.php/NLopt_Algorithms

       -c name, --class name
              list of class names.

       -r value, --reclass value
              list of class values (use same order as in --class option).

                                         24 January 2016                              pkoptsvm(1)