it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive

Class PassiveAggressiveRegression

java.lang.Object

it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive

it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression

All Implemented Interfaces:

BinaryLearningAlgorithm, LearningAlgorithm, OnlineLearningAlgorithm, RegressionLearningAlgorithm

Direct Known Subclasses:

KernelizedPassiveAggressiveRegression, LinearPassiveAggressiveRegression

public abstract class PassiveAggressiveRegression
extends PassiveAggressive
implements RegressionLearningAlgorithm

Online Passive-Aggressive Learning Algorithm for regression tasks. reference:

[CrammerJLMR2006] Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz and Yoram Singer Online Passive-Aggressive Algorithms. Journal of Machine Learning Research (2006)

Author:

Simone Filice

Nested Class Summary

Nested classes/interfaces inherited from class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive

PassiveAggressive.Policy

Field Summary

Fields

Modifier and Type	Field and Description
protected float	epsilon
protected UnivariateRegressionFunction	regressor

Fields inherited from class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive

c, label, policy

Constructor Summary

Constructors

Constructor and Description
PassiveAggressiveRegression()

Method Summary

Methods

Modifier and Type	Method and Description
float	getEpsilon() Returns epsilon, i.e.
UnivariateRegressionFunction	getPredictionFunction() Returns the regressor learned during the training process
void	learn(Dataset dataset) It starts the training process exploiting the provided dataset
UnivariateRegressionOutput	learn(Example example) Applies the learning process on a single example, updating its current model
void	setEpsilon(float epsilon) Sets epsilon, i.e.

Methods inherited from class it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive

computeWeight, getC, getLabel, getLabels, getPolicy, reset, setC, setLabel, setLabels, setPolicy

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

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

duplicate, getLabels, reset, setLabels

Field Detail

regressor

protected UnivariateRegressionFunction regressor

epsilon

protected float epsilon

Constructor Detail

PassiveAggressiveRegression

public PassiveAggressiveRegression()

Method Detail

getEpsilon

public float getEpsilon()

Returns epsilon, i.e. the accepted distance between the predicted and the real regression values

Returns:

the epsilon

setEpsilon

public void setEpsilon(float epsilon)

Sets epsilon, i.e. the accepted distance between the predicted and the real regression values

Parameters:

epsilon - the epsilon to set

getPredictionFunction

public UnivariateRegressionFunction getPredictionFunction()

Description copied from interface: RegressionLearningAlgorithm

Returns the regressor learned during the training process

Specified by:

getPredictionFunction in interface LearningAlgorithm

Specified by:

getPredictionFunction in interface RegressionLearningAlgorithm

Returns:

the regressor learned during the training process

learn

public void learn(Dataset dataset)

Description copied from interface: LearningAlgorithm

It starts the training process exploiting the provided dataset

Specified by:

learn in interface LearningAlgorithm

Overrides:

learn in class PassiveAggressive

Parameters:

dataset - the training data

learn

public UnivariateRegressionOutput learn(Example example)

Description copied from interface: OnlineLearningAlgorithm

Applies the learning process on a single example, updating its current model

Specified by:

learn in interface OnlineLearningAlgorithm

Parameters:

example - the instance to be exploited in the learning process

Returns:

the Prediction associated to example before the updating step