Machine Learning

Namespaces
	vigra::rf3
	Random forest version 3.
	vigra::rf::visitors
	Visitors to extract information during training of vigra::RandomForest version 2.

Classes
class	DepthStop
	Random forest 'maximum depth' stop criterion. More...
class	EntropyScore
	Functor that computes the entropy score. More...
class	GiniScore
	Functor that computes the gini score. More...
class	KolmogorovSmirnovScore
	Functor that computes the Kolmogorov-Smirnov score. More...
class	NodeComplexityStop
	Random forest 'node complexity' stop criterion. More...
class	NumInstancesStop
	Random forest 'number of datapoints' stop criterion. More...
class	ProblemSpec< LabelType >
	problem specification class for the random forest. More...
class	PurityStop
	Random forest 'node purity' stop criterion. More...
class	RandomForest< LabelType, PreprocessorTag >
	Random forest version 2 (see also vigra::rf3::RandomForest for version 3) More...
class	RandomForestOptions
	Options object for the random forest. More...
class	Sampler< Random >
	Create random samples from a sequence of indices. More...
class	SamplerOptions
	Options object for the Sampler class. More...

Detailed Description

This module provides classification algorithms that map features to labels or label probabilities. Look at the vigra::RandomForest class (for implementation version 2) or the vigra::rf3::random_forest() factory function (for implementation version 3) for an overview of the functionality as well as use cases.