LearnParameters Class Reference

The set of parameters that controls how a classifier is being learned from a training set. More...

Static Public Attributes
const int	IndifferenceRadiusDefault = 6
	Default value for the IndifferenceRadius.
const double	NegativesDensityDefault = 1.0
	Default value for NegativesDensity.
const int	ContrastTriggerDefault = 8
	Default value for ContrastTrigger.
const int	EnsembleSizeDefault = 15
	Default value for EnsembleSize.
const int	PolydromyDefault = 2
	Default value for Polydromy.
const int	MinFeatureCountDefault = 50
	Default value for MinFeatureCount.

Properties
int	MinFeatureCount [get, set]
	This value defines how many features must be extracted at the very least per class in a classifier. More...
int	ContrastTrigger [get, set]
	Contrast trigger is the minimum gray value difference the two regions of one pair feature must have to be eligible to become a classifier feature. Valid contrast triggers must be greater than 0, the default value is 8. More...
int	IndifferenceRadius [get, set]
	Indifference radius is the radius (L1 norm!) around a positive instance from which no counter sample is to be extracted. Valid indifference radii must be greater than 0. The default value is 6 here. More...
double	NegativesDensity [get, set]
	Negatives density is the density at which counter samples are extracted from the training set images during the learning phase. Valid negatives densities range from 0.0 to 1.0. The default value is 1.0. More...

Detailed Description

The set of parameters that controls how a classifier is being learned from a training set.

Property Documentation

ContrastTrigger

int ContrastTrigger

getset

Contrast trigger is the minimum gray value difference the two regions of one pair feature must have to be eligible to become a classifier feature. Valid contrast triggers must be greater than 0, the default value is 8.

Exceptions

ArgumentOutOfRangeException

when trying to set a value less than 1

IndifferenceRadius

int IndifferenceRadius

getset

Indifference radius is the radius (L1 norm!) around a positive instance from which no counter sample is to be extracted. Valid indifference radii must be greater than 0. The default value is 6 here.

Exceptions

ArgumentOutOfRangeException

when trying to set a value less than 1

MinFeatureCount

int MinFeatureCount

getset

This value defines how many features must be extracted at the very least per class in a classifier.

Exceptions

ArgumentOutOfRangeException

when trying to set a value less than 1

NegativesDensity

double NegativesDensity

getset

Negatives density is the density at which counter samples are extracted from the training set images during the learning phase. Valid negatives densities range from 0.0 to 1.0. The default value is 1.0.

Exceptions

ArgumentOutOfRangeException

when trying to set a value less than 0.0 or greater than 1.0