it.uniroma2.sag.kelp.learningalgorithm

Interface BinaryLearningAlgorithm

All Superinterfaces:

LearningAlgorithm

All Known Implementing Classes:

BinaryCSvmClassification, BinaryNuSvmClassification, BudgetedLearningAlgorithm, BudgetedPassiveAggressiveClassification, DCDLearningAlgorithm, EpsilonSvmRegression, KernelizedPassiveAggressiveClassification, KernelizedPassiveAggressiveRegression, KernelizedPerceptron, LibCSvmSolver, LibLinearLearningAlgorithm, LibLinearRegression, LibNuSvmSolver, LibSvmSolver, LinearPassiveAggressiveClassification, LinearPassiveAggressiveRegression, LinearPerceptron, OneClassSvmClassification, PassiveAggressive, PassiveAggressiveClassification, PassiveAggressiveRegression, PegasosLearningAlgorithm, Perceptron, RandomizedBudgetPerceptron, SoftConfidenceWeightedClassification, Stoptron

public interface BinaryLearningAlgorithm
extends LearningAlgorithm

It is a learning algorithm that has to learn a concept associated to a single label. Examples of binaryLearningAlgorithm are:

• one-classSVM
• a binary classification learning algorithm (all the examples not associated to the label to be learned are considered negative examples)
• a uni-variate regression algorithm, where the property to be learn is a single label

Author:

Simone Filice

Method Summary

Methods

Modifier and Type	Method and Description
Label	getLabel()
List<Label>	getLabels() Returns the labels representing the concept to be learned.
void	setLabel(Label label)
void	setLabels(List<Label> labels) Sets the labels representing the concept to be learned.

Methods inherited from interface it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithm

duplicate, getPredictionFunction, learn, reset

Method Detail

getLabel

Label getLabel()

Returns:

the label associated to the positive class

setLabel

void setLabel(Label label)

Parameters:

positiveClass - the label associated to the positive class

setLabels

void setLabels(List<Label> labels)

Description copied from interface: LearningAlgorithm

Sets the labels representing the concept to be learned.

Specified by:

setLabels in interface LearningAlgorithm

Parameters:

positiveClass - the label associated to the positive class, i.e. the list must contain a single entry

getLabels

List<Label> getLabels()

Description copied from interface: LearningAlgorithm

Returns the labels representing the concept to be learned.

Specified by:

getLabels in interface LearningAlgorithm

Returns:

the label associated to the positive class, i.e. the output list contains a single entry