pkoptsvm - program to optimize parameters for SVM classification

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  pa‐
       rameters are {cost} and {gamma}.  The utility pkoptsvm can optimize these two parameters, based on an ac‐
       curacy  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.

       pkoptsvm - program to optimize parameters for SVM classification

-tfilename, --trainingfilename
              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).

       -ifilename, --inputfilename
              input test vector file

       -ccstartvalue-ccendvalue, --ccoststartvalue--ccostendvalue
              min and max boundaries the parameter C of C-SVC, epsilon-SVR, and nu-SVR (optional: initial value)

       -gstartvalue-gendvalue, --gammastartvalue--gammaendvalue
              min max boundaries for gamma in kernel function (optional: initial value)

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

       -vlevel, --verboselevel
              use 1 to output intermediate results for plotting

       Advanced options

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

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

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

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

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

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

       -sbandband, --startbandband
              start band sequence number

       -ebandband, --endbandband
              end band sequence number

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

       -scalevalue, --scalevalue
              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)

       -svmttype, --svmtypetype
              type of SVM (C_SVC, nu_SVC,one_class, epsilon_SVR, nu_SVR)

       -kttype, --kerneltypetype
              type of kernel function (linear,polynomial,radial,sigmoid)

       -kdvalue, --kdvalue
              degree in kernel function

       -c0value, --coef0value
              coef0 in kernel function

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

       -elossvalue, --elossvalue
              the epsilon in loss function of epsilon-SVR

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

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

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

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

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

       -maxitnumber, --maxitnumber
              maximum number of iterations

       -tolvalue, --tolerancevalue
              relative tolerance for stopping criterion (default: 0.0001)

       -avalue, --algorithmvalue
              GRID, or any optimization algorithm from http://ab-initio.mit.edu/wiki/index.php/NLopt_Algorithms-cname, --classname
              list of class names.

       -rvalue, --reclassvalue
              list of class values (use same order as in --class option).

                                                 01 January 2025                                     pkoptsvm(1)

pkoptsvm-ttraining [options] [advancedoptions]