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.TH pkoptsvm 1 "06 December 2020" "" ""
.SH NAME
pkoptsvm \- program to optimize parameters for SVM classification
.SH SYNOPSIS
'nh
.fi
.ad l
\fBpkoptsvm\fR \kx
.if (\nx>(\n(.l/2)) .nr x (\n(.l/5)
'in \n(.iu+\nxu
\fB-t\fR \fItraining\fR [\fIoptions\fR] [\fIadvanced options\fR]
'in \n(.iu-\nxu
.ad b
'hy
.SH DESCRIPTION
\fBpkoptsvm\fR 
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 \fBpkoptsvm\fR can optimize these two parameters,
based on an accuracy assessment (the Kappa value).
If an input test set (\*(T<\fB\-i\fR\*(T>) is provided, it is used for
the accuracy assessment.
If not, the accuracy assessment is based on a cross validation
(\*(T<\fB\-cv\fR\*(T>) of the training sample.
.PP
The optimization routine uses a grid search.
The initial and final values of the parameters can be set with
\*(T<\fB\-cc\fR\*(T> startvalue \*(T<\fB\-cc\fR\*(T> endvalue and
\*(T<\fB\-g\fR\*(T> startvalue \*(T<\fB\-g\fR\*(T> endvalue for cost
and gamma respectively.
The search uses a multiplicative step for iterating the parameters
(set with the options \*(T<\fB\-stepcc\fR\*(T> and
\*(T<\fB\-stepg\fR\*(T>).
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.
.SH OPTIONS
.TP 
\*(T<\fB\-t\fR\*(T> \fIfilename\fR, \*(T<\fB\-\-training\fR\*(T> \fIfilename\fR
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).
.TP 
\*(T<\fB\-i\fR\*(T> \fIfilename\fR, \*(T<\fB\-\-input\fR\*(T> \fIfilename\fR
input test vector file
.TP 
\*(T<\fB\-cc\fR\*(T> \fIstartvalue\fR \*(T<\fB\-cc\fR\*(T> \fIendvalue\fR, \*(T<\fB\-\-ccost\fR\*(T> \fIstartvalue\fR \*(T<\fB\-\-ccost\fR\*(T> \fIendvalue\fR
min and max boundaries the parameter C of C-SVC, epsilon-SVR,
and nu-SVR (optional: initial value)
.TP 
\*(T<\fB\-g\fR\*(T> \fIstartvalue\fR \*(T<\fB\-g\fR\*(T> \fIendvalue\fR, \*(T<\fB\-\-gamma\fR\*(T> \fIstartvalue\fR \*(T<\fB\-\-gamma\fR\*(T> \fIendvalue\fR
min max boundaries for gamma in kernel function
(optional: initial value)
.TP 
\*(T<\fB\-step\fR\*(T> \fIstepsize\fR, \*(T<\fB\-\-step\fR\*(T> \fIstepsize\fR
multiplicative step for ccost and gamma in GRID search
.TP 
\*(T<\fB\-v\fR\*(T> \fIlevel\fR, \*(T<\fB\-\-verbose\fR\*(T> \fIlevel\fR
use 1 to output intermediate results for plotting
.PP
Advanced options
.TP 
\*(T<\fB\-tln\fR\*(T> \fIlayer\fR, \*(T<\fB\-\-tln\fR\*(T> \fIlayer\fR
training layer name(s)
.TP 
\*(T<\fB\-label\fR\*(T> \fIattribute\fR, \*(T<\fB\-\-label\fR\*(T> \fIattribute\fR
identifier for class label in training vector file.
(default: label)
.TP 
\*(T<\fB\-bal\fR\*(T> \fIsize\fR, \*(T<\fB\-\-balance\fR\*(T> \fIsize\fR
balance the input data to this number of samples for each class
(default: 0)
.TP 
\*(T<\fB\-random\fR\*(T>, \*(T<\fB\-\-random\fR\*(T>
in case of balance, randomize input data
.TP 
\*(T<\fB\-min\fR\*(T> \fInumber\fR, \*(T<\fB\-\-min\fR\*(T> \fInumber\fR
if number of training pixels is less then min,
do not take this class into account
.TP 
\*(T<\fB\-b\fR\*(T> \fIband\fR, \*(T<\fB\-\-band\fR\*(T> \fIband\fR
band index (starting from 0, either use band option or use start
to end)
.TP 
\*(T<\fB\-sband\fR\*(T> \fIband\fR, \*(T<\fB\-\-startband\fR\*(T> \fIband\fR
start band sequence number
.TP 
\*(T<\fB\-eband\fR\*(T> \fIband\fR, \*(T<\fB\-\-endband\fR\*(T> \fIband\fR
end band sequence number
.TP 
\*(T<\fB\-offset\fR\*(T> \fIvalue\fR, \*(T<\fB\-\-offset\fR\*(T> \fIvalue\fR
offset value for each spectral band input features:
\*(T<refl[band]=(DN[band]\-offset[band])/scale[band]\*(T>
.TP 
\*(T<\fB\-scale\fR\*(T> \fIvalue\fR, \*(T<\fB\-\-scale\fR\*(T> \fIvalue\fR
scale value for each spectral band input features:
\*(T<refl=(DN[band]\-offset[band])/scale[band]\*(T>
(use \*(T<0\*(T> if scale min and max in each band to
\*(T<\-1.0\*(T> and \*(T<1.0\*(T>)
.TP 
\*(T<\fB\-svmt\fR\*(T> \fItype\fR, \*(T<\fB\-\-svmtype\fR\*(T> \fItype\fR
type of SVM (C_SVC, nu_SVC,one_class, epsilon_SVR, nu_SVR)
.TP 
\*(T<\fB\-kt\fR\*(T> \fItype\fR, \*(T<\fB\-\-kerneltype\fR\*(T> \fItype\fR
type of kernel function (linear,polynomial,radial,sigmoid)
.TP 
\*(T<\fB\-kd\fR\*(T> \fIvalue\fR, \*(T<\fB\-\-kd\fR\*(T> \fIvalue\fR
degree in kernel function
.TP 
\*(T<\fB\-c0\fR\*(T> \fIvalue\fR, \*(T<\fB\-\-coef0\fR\*(T> \fIvalue\fR
coef0 in kernel function
.TP 
\*(T<\fB\-nu\fR\*(T> \fIvalue\fR, \*(T<\fB\-\-nu\fR\*(T> \fIvalue\fR
the parameter nu of nu-SVC, one-class SVM, and nu-SVR
.TP 
\*(T<\fB\-eloss\fR\*(T> \fIvalue\fR, \*(T<\fB\-\-eloss\fR\*(T> \fIvalue\fR
the epsilon in loss function of epsilon-SVR
.TP 
\*(T<\fB\-cache\fR\*(T> \fInumber\fR, \*(T<\fB\-\-cache\fR\*(T> \fInumber\fR
cache memory size in MB
(default: 100)
.TP 
\*(T<\fB\-etol\fR\*(T> \fIvalue\fR, \*(T<\fB\-\-etol\fR\*(T> \fIvalue\fR
the tolerance of termination criterion
(default: 0.001)
.TP 
\*(T<\fB\-shrink\fR\*(T>, \*(T<\fB\-\-shrink\fR\*(T>
whether to use the shrinking heuristics 
.TP 
\*(T<\fB\-cv\fR\*(T> \fIvalue\fR, \*(T<\fB\-\-cv\fR\*(T> \fIvalue\fR
n-fold cross validation mode (default: 0)
.TP 
\*(T<\fB\-cf\fR\*(T>, \*(T<\fB\-\-cf\fR\*(T>
use Overall Accuracy instead of kappa
.TP 
\*(T<\fB\-maxit\fR\*(T> \fInumber\fR, \*(T<\fB\-\-maxit\fR\*(T> \fInumber\fR
maximum number of iterations
.TP 
\*(T<\fB\-tol\fR\*(T> \fIvalue\fR, \*(T<\fB\-\-tolerance\fR\*(T> \fIvalue\fR
relative tolerance for stopping criterion
(default: 0.0001)
.TP 
\*(T<\fB\-a\fR\*(T> \fIvalue\fR, \*(T<\fB\-\-algorithm\fR\*(T> \fIvalue\fR
GRID, or any optimization algorithm from
http://ab-initio.mit.edu/wiki/index.php/NLopt_Algorithms
.TP 
\*(T<\fB\-c\fR\*(T> \fIname\fR, \*(T<\fB\-\-class\fR\*(T> \fIname\fR
list of class names.
.TP 
\*(T<\fB\-r\fR\*(T> \fIvalue\fR, \*(T<\fB\-\-reclass\fR\*(T> \fIvalue\fR
list of class values (use same order as in
\*(T<\fB\-\-class\fR\*(T> option).