it.uniroma2.sag.kelp.learningalgorithm

Interface LearningAlgorithm

All Known Subinterfaces:

BinaryLearningAlgorithm, ClassificationLearningAlgorithm, EnsembleLearningAlgorithm, MetaLearningAlgorithm, OnlineLearningAlgorithm, RegressionLearningAlgorithm

All Known Implementing Classes:

BinaryCSvmClassification, BinaryNuSvmClassification, BudgetedLearningAlgorithm, BudgetedPassiveAggressiveClassification, DCDLearningAlgorithm, EpsilonSvmRegression, KernelizedPassiveAggressiveClassification, KernelizedPassiveAggressiveRegression, KernelizedPerceptron, LibCSvmSolver, LibLinearLearningAlgorithm, LibLinearRegression, LibNuSvmSolver, LibSvmSolver, LinearPassiveAggressiveClassification, LinearPassiveAggressiveRegression, LinearPerceptron, MultiEpochLearning, MultiLabelClassificationLearning, OneClassSvmClassification, OneVsAllLearning, OneVsOneLearning, PassiveAggressive, PassiveAggressiveClassification, PassiveAggressiveRegression, PegasosLearningAlgorithm, Perceptron, RandomizedBudgetPerceptron, SequenceClassificationKernelBasedLearningAlgorithm, SequenceClassificationLearningAlgorithm, SequenceClassificationLinearLearningAlgorithm, SoftConfidenceWeightedClassification, Stoptron

public interface LearningAlgorithm

It is a generic Machine Learning algorithm

Author:

Simone Filice

Method Summary

Methods

Modifier and Type	Method and Description
LearningAlgorithm	duplicate() Creates a new instance of the LearningAlgorithm initialized with the same parameters of the learningAlgorithm to be duplicated.
List<Label>	getLabels() Returns the labels representing the concept to be learned.
PredictionFunction	getPredictionFunction() Returns the predictionFunction learned during the training process
void	learn(Dataset dataset) It starts the training process exploiting the provided dataset
void	reset() Resets all the learning process, returning to the default state.
void	setLabels(List<Label> labels) Sets the labels representing the concept to be learned.

Method Detail

learn

void learn(Dataset dataset)

It starts the training process exploiting the provided dataset

Parameters:

dataset - the training data

setLabels

void setLabels(List<Label> labels)

Sets the labels representing the concept to be learned.

Parameters:

labels - the labels representing the concept to be learned

getLabels

List<Label> getLabels()

Returns the labels representing the concept to be learned.

Returns:

the labels representing the concept to be learned

duplicate

LearningAlgorithm duplicate()

Creates a new instance of the LearningAlgorithm initialized with the same parameters of the learningAlgorithm to be duplicated. A shallow copy of the learning parameters is performed, while the state parameters are initialized to their default value. The labels are not initialized neither copied

reset

void reset()

Resets all the learning process, returning to the default state. All the learned process is forgotten

getPredictionFunction

PredictionFunction getPredictionFunction()

Returns the predictionFunction learned during the training process

Returns:

the predictionFunction learned during the training process