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

Class KernelizedPassiveAggressiveRegression

java.lang.Object

it.uniroma2.sag.kelp.learningalgorithm.PassiveAggressive

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

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

All Implemented Interfaces:

BinaryLearningAlgorithm, KernelMethod, LearningAlgorithm, OnlineLearningAlgorithm, RegressionLearningAlgorithm

public class KernelizedPassiveAggressiveRegression
extends PassiveAggressiveRegression
implements KernelMethod

Online Passive-Aggressive Learning Algorithm for regression tasks (kernel machine version). 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 inherited from class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression

epsilon, regressor

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

c, label, policy

Constructor Summary

Constructors

Constructor and Description
KernelizedPassiveAggressiveRegression()
KernelizedPassiveAggressiveRegression(float aggressiveness, float epsilon, PassiveAggressive.Policy policy, Kernel kernel, Label label)

Method Summary

Methods

Modifier and Type	Method and Description
KernelizedPassiveAggressiveRegression	duplicate() Creates a new instance of the LearningAlgorithm initialized with the same parameters of the learningAlgorithm to be duplicated.
Kernel	getKernel() Returns the kernel exploited by this learner
UnivariateKernelMachineRegressionFunction	getPredictionFunction() Returns the regressor learned during the training process
void	setKernel(Kernel kernel) Sets the kernel this
void	setPredictionFunction(PredictionFunction predictionFunction) Sets the predictionFunction learned during the training process.

Methods inherited from class it.uniroma2.sag.kelp.learningalgorithm.regression.passiveaggressive.PassiveAggressiveRegression

getEpsilon, learn, learn, setEpsilon

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

getLabels, reset, setLabels

Constructor Detail

KernelizedPassiveAggressiveRegression

public KernelizedPassiveAggressiveRegression()

KernelizedPassiveAggressiveRegression

public KernelizedPassiveAggressiveRegression(float aggressiveness,
                                     float epsilon,
                                     PassiveAggressive.Policy policy,
                                     Kernel kernel,
                                     Label label)

Method Detail

getKernel

public Kernel getKernel()

Description copied from interface: KernelMethod

Returns the kernel exploited by this learner

Specified by:

getKernel in interface KernelMethod

Returns:

the kernel

setKernel

public void setKernel(Kernel kernel)

Description copied from interface: KernelMethod

Sets the kernel this

Specified by:

setKernel in interface KernelMethod

Parameters:

kernel - the kernel to set

duplicate

public KernelizedPassiveAggressiveRegression duplicate()

Description copied from interface: LearningAlgorithm

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

Specified by:

duplicate in interface LearningAlgorithm

getPredictionFunction

public UnivariateKernelMachineRegressionFunction 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

Overrides:

getPredictionFunction in class PassiveAggressiveRegression

Returns:

the regressor learned during the training process

setPredictionFunction

public void setPredictionFunction(PredictionFunction predictionFunction)

Description copied from interface: LearningAlgorithm

Sets the predictionFunction learned during the training process. It can be used to change the default prediction function, or to set an already trained one.

Specified by:

setPredictionFunction in interface LearningAlgorithm