LearnParameters Class

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.

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.

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

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.

Determines whether the specified Object is equal to the current Object.

Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.

Serves as a hash function for a particular type.

Gets the Type of the current instance.

Creates a shallow copy of the current Object.

Returns a string that represents the current object.

Default value for ContrastTrigger.

Default value for EnsembleSize.

Default value for the IndifferenceRadius.

Default value for MinFeatureCount.

Default value for NegativesDensity.

Default value for Polydromy.