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PROGRAM:

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)

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