program to optimize parameters for support vector machine classifier pksvm

SYNOPSIS

Usage: pkoptsvm -t training

Options: [-cc startvalue -cc endvalue] [-g startvalue -g endvalue] [-stepcc stepsize] [-stepg stepsize]

Advanced options:

Description

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

• use either -short or --long options (both --long=value and --long value are supported)
• short option -h shows basic options only, long option --help shows all options

  short	long	type	default	description
  t	training	std::string		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).
  cc	ccost	float	1	min and max boundaries the parameter C of C-SVC, epsilon-SVR, and nu-SVR (optional: initial value)
  g	gamma	float	0	min max boundaries for gamma in kernel function (optional: initial value)
  stepcc	stepcc	double	2	multiplicative step for ccost in GRID search
  stepg	stepg	double	2	multiplicative step for gamma in GRID search
  i	input	std::string		input test vector file
  tln	tln	std::string		training layer name(s)
  label	label	std::string	label	identifier for class label in training vector file.
  bal	balance	unsigned int	0	balance the input data to this number of samples for each class
  random	random	bool	true	in case of balance, randomize input data
  min	min	int	0	if number of training pixels is less then min, do not take this class into account
  b	band	unsigned short		band index (starting from 0, either use band option or use start to end)
  sband	startband	unsigned short		Start band sequence number
  eband	endband	unsigned short		End band sequence number
  offset	offset	double	0	offset value for each spectral band input features: refl[band]=(DN[band]-offset[band])/scale[band]
  scale	scale	double	0	scale value for each spectral band input features: refl=(DN[band]-offset[band])/scaleband
  svmt	svmtype	std::string	C_SVC	type of SVM (C_SVC, nu_SVC,one_class, epsilon_SVR, nu_SVR)
  kt	kerneltype	std::string	radial	type of kernel function (linear,polynomial,radial,sigmoid)
  kd	kd	unsigned short	3	degree in kernel function
  c0	coef0	float	0	coef0 in kernel function
  nu	nu	float	0.5	the parameter nu of nu-SVC, one-class SVM, and nu-SVR
  eloss	eloss	float	0.1	the epsilon in loss function of epsilon-SVR
  cache	cache	int	100	cache memory size in MB
  etol	etol	float	0.001	the tolerance of termination criterion
  shrink	shrink	bool	false	whether to use the shrinking heuristics
  pe	probest	bool	true	whether to train a SVC or SVR model for probability estimates
  cv	cv	unsigned short	2	n-fold cross validation mode
  cf	cf	bool	false	use Overall Accuracy instead of kappa
  maxit	maxit	unsigned int	500	maximum number of iterations
  tol	tolerance	double	0.0001	relative tolerance for stopping criterion
  c	class	std::string		list of class names.
  r	reclass	short		list of class values (use same order as in class opt).

  Usage: pkoptsvm -t training