pkoptsvm(1)

NAME
pkoptsvm - program to optimize parameters for SVM classification

SYNOPSIS
\FCpkoptsvm\F[]
\FC-t\F[] 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 (\FC-i\F[]) is provided, it is used for the accuracy assessment. If not, the accuracy assessment is based on a cross validation (\FC-cv\F[]) of the training sample.

The optimization routine uses a grid search. The initial and final values of the parameters can be set with \FC-cc\F[] startvalue \FC-cc\F[] endvalue and \FC-g\F[] startvalue \FC-g\F[] endvalue for cost and gamma respectively. The search uses a multiplicative step for iterating the parameters (set with the options \FC-stepcc\F[] and \FC-stepg\F[]). 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

\FC-t\F[] filename, \FC--training\F[] 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).

\FC-i\F[] filename, \FC--input\F[] filename
    input test vector file

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

\FC-g\F[] startvalue \FC-g\F[] endvalue, \FC--gamma\F[] startvalue \FC--gamma\F[] endvalue
    min max boundaries for gamma in kernel function (optional: initial value)

\FC-step\F[] stepsize, \FC--step\F[] stepsize
    multiplicative step for ccost and gamma in GRID search

\FC-v\F[] level, \FC--verbose\F[] level
    use 1 to output intermediate results for plotting

Advanced options

\FC-tln\F[] layer, \FC--tln\F[] layer
    training layer name(s)

\FC-label\F[] attribute, \FC--label\F[] attribute
    identifier for class label in training vector file. (default: label)

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

\FC-random\F[], \FC--random\F[]
    in case of balance, randomize input data

\FC-min\F[] number, \FC--min\F[] number
    if number of training pixels is less then min, do not take this class into account

\FC-b\F[] band, \FC--band\F[] band
    band index (starting from 0, either use band option or use start to end)

\FC-sband\F[] band, \FC--startband\F[] band
    start band sequence number

\FC-eband\F[] band, \FC--endband\F[] band
    end band sequence number

\FC-offset\F[] value, \FC--offset\F[] value
    offset value for each spectral band input features: \FCrefl[band]=(DN[band]-offset[band])/scale[band]\F[]

\FC-scale\F[] value, \FC--scale\F[] value
    scale value for each spectral band input features: \FCrefl=(DN[band]-offset[band])/scale[band]\F[] (use \FC0\F[] if scale min and max in each band to \FC-1.0\F[] and \FC1.0\F[])

\FC-svmt\F[] type, \FC--svmtype\F[] type
    type of SVM (C_SVC, nu_SVC,one_class, epsilon_SVR, nu_SVR)

\FC-kt\F[] type, \FC--kerneltype\F[] type
    type of kernel function (linear,polynomial,radial,sigmoid)

\FC-kd\F[] value, \FC--kd\F[] value
    degree in kernel function

\FC-c0\F[] value, \FC--coef0\F[] value
    coef0 in kernel function

\FC-nu\F[] value, \FC--nu\F[] value
    the parameter nu of nu-SVC, one-class SVM, and nu-SVR

\FC-eloss\F[] value, \FC--eloss\F[] value
    the epsilon in loss function of epsilon-SVR

\FC-cache\F[] number, \FC--cache\F[] number
    cache memory size in MB (default: 100)

\FC-etol\F[] value, \FC--etol\F[] value
    the tolerance of termination criterion (default: 0.001)

\FC-shrink\F[], \FC--shrink\F[]
    whether to use the shrinking heuristics

\FC-cv\F[] value, \FC--cv\F[] value
    n-fold cross validation mode (default: 0)

\FC-cf\F[], \FC--cf\F[]
    use Overall Accuracy instead of kappa

\FC-maxit\F[] number, \FC--maxit\F[] number
    maximum number of iterations

\FC-tol\F[] value, \FC--tolerance\F[] value
    relative tolerance for stopping criterion (default: 0.0001)

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

\FC-c\F[] name, \FC--class\F[] name
    list of class names.

\FC-r\F[] value, \FC--reclass\F[] value
    list of class values (use same order as in \FC--class\F[] option).